WO2006031536A2 - Retour d'information de tutorat intelligent - Google Patents

Retour d'information de tutorat intelligent Download PDF

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Publication number
WO2006031536A2
WO2006031536A2 PCT/US2005/031769 US2005031769W WO2006031536A2 WO 2006031536 A2 WO2006031536 A2 WO 2006031536A2 US 2005031769 W US2005031769 W US 2005031769W WO 2006031536 A2 WO2006031536 A2 WO 2006031536A2
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WO
WIPO (PCT)
Prior art keywords
word
words
user
audio
intervention
Prior art date
Application number
PCT/US2005/031769
Other languages
English (en)
Other versions
WO2006031536A3 (fr
Inventor
Valerie L. Beattie
Marilyn Jager Adams
Michael Barrow
Original Assignee
Soliloquy Learning, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US10/939,295 external-priority patent/US20060069562A1/en
Priority claimed from US10/938,762 external-priority patent/US9520068B2/en
Priority claimed from US10/938,748 external-priority patent/US7433819B2/en
Priority claimed from US10/938,746 external-priority patent/US8109765B2/en
Application filed by Soliloquy Learning, Inc. filed Critical Soliloquy Learning, Inc.
Publication of WO2006031536A2 publication Critical patent/WO2006031536A2/fr
Publication of WO2006031536A3 publication Critical patent/WO2006031536A3/fr

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Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass
    • G09B19/04—Speaking
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/225—Feedback of the input speech

Definitions

  • Reading software tends to focus on reading skills other than reading fluency. A few reading software products claim to provide benefit for developing reading fluency. One component in developing reading fluency is developing rapid and correct recognition and pronunciation of words included in a passage.
  • a computer-based method includes receiving audio input associated with a user reading a sequence of words displayed on a graphical user interface. The method also includes assessing a level of fluency and pronunciation accuracy of a user's reading of the sequence of words using speech recognition technology to compare the audio input with an expected sequence of words. The method includes providing feedback to the user related to the level of fluency and pronunciation accuracy for a word.
  • the feedback can include immediate feedback if the level of fluency and pronunciation accuracy for a word does not meet a first set of criteria.
  • the feedback can further include additional, deferred feedback to the user if immediate feedback was given for the word, depending on the type of immediate feedback given, the category of the word, and other criteria.
  • the feedback can further include deferred feedback to the user if the level of fluency and pronunciation accuracy for a word meets the first set of criteria, but does not meet a second set of fluency and pronunciation accuracy criteria.
  • a user can therefore receive only immediate feedback, only deferred feedback, or both immediate and deferred feedback for a word based on the fluency and pronunciation accuracy criteria.
  • Embodiments can include one or more of the following.
  • Providing immediate feedback can include providing an intervention if a specified time period since the start of the audio input associated with a sequence of words has elapsed, and the speech recognition process has not identified the first word in the sequence.
  • Providing immediate feedback can include providing an intervention if a specified time period since identifying the previous word in the sequence of words has elapsed and the speech recognition process has not identified the word.
  • Providing deferred feedback can include placing the word on a review list and/or coloring the text of the word.
  • Providing deferred feedback to the user can include representing the user's pronunciation of a word by an acoustic match confidence level and providing feedback if the acoustic match confidence level is below a specified target level.
  • Providing deferred feedback to the user can include providing feedback based on the elapsed time before, during, and/or after the audio identified as a word.
  • the criteria for providing immediate and deferred feedback for a word can be dependent on a word category associated with the word.
  • the interactive feedback can include a visual intervention such as a visual indication provided on the graphical user interface.
  • the interactive feedback can include an audio intervention such as an audio indication.
  • the visual indication can include highlighting the word on the user interface.
  • the visual indication can include coloring the text prior to the word in the passage in a first color and coloring the word and text subsequent to the word in the sentence in a second color.
  • the audio indication can include a pronunciation of the word.
  • a visual intervention can be provided based on the user's first attempt to read a word and an audio intervention can be provided based on the user's second attempt to read the word.
  • the method can also include providing the user a third opportunity to read the word subsequent to the audio intervention and providing a visual indication on the graphical user interface to indicate that the user should continue reading with the subsequent word if the fluency or pronunciation accuracy criteria are not met.
  • the deferred feedback can include coloring the sequence of words read by the user using different colors on the graphical user interface.
  • the method can also include providing a color based indication for words which met both sets of criteria using a first color, providing a color-based indication for words which received an audio intervention using a second color, and providing a color-based indication for words which did not receive an audio intervention and did not meet the deferred feedback criteria using a third color.
  • the method can include providing a color-based indication for words which received a visual intervention using the third color.
  • Providing additional, deferred feedback can include placing words on a review list. Words which received an audio intervention and/or a visual intervention can be placed on a review list.
  • the method can also include generating review exercises, which may be interactive or non-interactive, for the user based on the review list.
  • a computer program product residing on a computer readable medium can include instructions for causing an electrical device to receive audio input associated with a user reading a sequence of words displayed on a graphical user interface.
  • the computer program product can also include instructions assess a level of fluency and pronunciation accuracy of a user's reading of the sequence of words using speech recognition technology to compare the audio input with an expected sequence of words.
  • the computer program product can also include instructions provide feedback to the user related to the level of fluency and pronunciation accuracy for a word.
  • the feedback can include instructions to provide immediate feedback if the level of fluency and pronunciation accuracy for a word does not meet a first set of criteria, instructions to provide deferred feedback to the user if the level of fluency and pronunciation accuracy for a word meets the first set of criteria, but does not meet a second set of fluency and pronunciation accuracy criteria, and instructions to provide deferred feedback to the user if immediate feedback was given for the word, depending on the word's category and type of immediate feedback given.
  • Embodiments can include one or more of the following.
  • the computer program product can include instructions for causing an electrical device to represent the user's pronunciation of a word by an acoustic match confidence level; and provide feedback if the acoustic match confidence level is below a specified target level.
  • the immediate feedback can be a visual intervention which includes visual indications provided on the graphical user interface, and which does not include audio indications.
  • the computer program product can include instructions for causing an electrical device to color the text prior to the word in the passage in a first color and color the word and text subsequent to the word in the sentence in a second color, the first color being different from the second color.
  • the visual intervention is provided based on the user's first attempt to read a word and the audio intervention is provided based on the user's second attempt to read the word.
  • the computer program product of claim 20 further comprising instructions for causing an electrical device to provide a color based indication for words which met both sets of fluency and pronunciation accuracy criteria using a first color, provide a color based indication for words which received an audio intervention using a second color, and provide a color based indication for words which did not receive an audio intervention and did not meet the second set of fluency and pronunciation accuracy criteria using a third color.
  • Words which received an audio intervention can be placed on a review list.
  • Words which received a visual intervention are placed on a review list.
  • a device can be configured to receive audio input associated with a user reading a sequence of words displayed on a graphical user interface, assess a level of fluency and pronunciation accuracy of a user's reading of the sequence of words using speech recognition technology to compare the audio input with an expected sequence of words; and provide feedback to the user related to the level of fluency and pronunciation accuracy for a word.
  • the device can be configured such that the feedback can include configurations to provide immediate feedback if the level of fluency and pronunciation accuracy for a word does not meet a first set of criteria, provide deferred feedback to the user if the level of fluency and pronunciation accuracy for a word meets the first set of criteria, but does not meet a second set of fluency and pronunciation accuracy criteria, and provide deferred feedback to the user if immediate feedback was given for the word, depending on the word's category and type of immediate feedback given.
  • the device can also be configured to represent the user's pronunciation of a word by an acoustic match confidence level; and provide feedback if the acoustic match confidence level is below a specified target level.
  • the device can also be configured to color the text prior to the word in the passage in a first color and color the word and text subsequent to the word in the sentence in a second color, the first color being different from the second color.
  • the device can also be configured to provide a color based indication for words which met both sets of fluency and pronunciation accuracy criteria using a first color, provide a color based indication for words which received an audio intervention using a second color, and provide a color based indication for words which did not receive an audio intervention and did not meet the second set of fluency and pronunciation accuracy criteria using a third color.
  • a computer-based method for analyzing reading fluency includes categorizing at least some words in a passage into word categories.
  • the word category for a particular word can be based on its difficulty relative to the reading level of the passage, or its difficulty relative to the reading level of the user, or its significance to the passage content or lesson focus, or its mastery given the prior reading history of the user.
  • the word categories can include at least a glue word category and one or more target word categories.
  • the method also includes generating different types of responses by the tutor software based on the word category associated with a particular word.
  • Embodiments can include one or more of the following.
  • the responses can include at least one of an intervention, color-coding of words, and placing words on a review list.
  • the method can also include generating an acoustic match confidence indication for a word based on a received audio input file and a stored statistical model for the word and requiring different acoustic match confidence scores for words in different word categories. Requiring different acoustic match confidence scores for words in different word categories can include requiring a higher acoustic match confidence score for a word in the target category than for a word that is in a different word category.
  • the method can also include an acoustical match confidence score that is calculated differently (uses a different weighting of inputs) for different word categories.
  • the method can include providing an intervention, color-coding the word, and placing the word in a review list based on the comparison of the acoustic match confidence score to a word category-specific threshold.
  • the acoustic match confidence may not be used for interventions, color-coding and review list status for some word categories.
  • the method uses automatic speech recognition and associated post ⁇ processing to determine if the user said a particular word and at what point in the audio they said it.
  • the method can include measuring a time gap before or surrounding the audio segment identified as a particular word in a received audio input file or buffer, and using a smaller time gap threshold for words in a target word category and using a larger time gap threshold for words that are in a different word category.
  • the time gap can consist of any combination of speech, silence, and non-speech sounds. If the time gap is greater than the word category-specific threshold, the tutoring software may trigger an intervention on the word, may color-code the word as warranting review by the student, and/or may place the word on a review list.
  • the time gap measurement may not be used for interventions, color-coding, and review list status for some word categories.
  • the tutoring software does not generate a visual intervention or audio intervention on a word in the glue word category if a valid recognition for a subsequent word in the sentence is received.
  • the method can include automatically color-coding words in the glue word category as read correctly, and not placing them on any review list.
  • the word categories can include a word category that includes words that are neither target words nor glue words.
  • Words in the passage can be categorized into one of the target word categories, the glue word category, and the word category that includes words that are neither target words nor glue words.
  • the target word categories are comprised of words that are judged to be especially difficult relative to the other words in the passage and words whose correct reading is judged to be especially important relative to the meaning of the passage or the focus of the lesson.
  • the target word categories can include words with less common usage or meanings than the other words in the passage, or words with a greater than average length or spelling-to- sound difficulty compared to other words in the passage.
  • the glue words can include short, common, function words that are likely to be unstressed in fluent reading of the sentence, and that are expected to be thoroughly familiar to the user.
  • Additional word categories may also be defined, such as a category consisting of words which the user has mastered based on the user's past reading history.
  • the time gap measurement may not be used to color code words or place words on the review list if the words are in the mastered word category. Instead, if the time gap measurement for the mastered word exceeds a threshold, it will be used as an indication that the user struggled with a different word in the sentence or with the overall interpretation of the sentence.
  • a computer program product residing on a computer- readable medium includes instructions for causing an electrical device to categorize at least some words in a passage into word categories according to each word's difficulty relative to the reading level of the passage, or its difficulty relative to the reading level of the user, or its significance to the passage content or lesson focus, or its mastery given the prior reading history of the user; and generate different types of responses by the tutor software based on the word category associated with a particular word.
  • Embodiments can include one or more of the following.
  • the computer program product can include instructions for causing an electrical device to generate an acoustic match confidence indication for a word based on a received audio input file and a stored statistical model for the word, and require different acoustic match confidence scores for words in different word categories.
  • the computer program product can include instructions for causing an electrical device to place the word in a review list based on the acoustic match confidence score.
  • the computer program product can include instructions for causing an electrical device to measure a time gap before or surrounding the audio segment identified via automatic speech recognition as a particular word in a received audio input file or buffer, use a time gap threshold that is specific to the word category of the word, and color code the word as "not correct” and/ or placing the word on a review list if the time gap is greater than the threshold.
  • a device is configured to categorize at least some words in a passage into word categories according to each word's difficulty relative to the reading level of the passage, or its difficulty relative to the reading level of the user, or its significance to the passage content or lesson focus, or its mastery given the prior reading history of the user.
  • the device is further configured to generate different types of responses by the tutor software based on the word category associated with a particular word.
  • Embodiments can include one or more of the following.
  • the device can be further configured to generate an acoustic match confidence indication for a word based on a received audio input file and a stored statistical model for the word and require different acoustic match confidence scores for words in different word categories.
  • the device can be further configured to determine placement of the word in a review list based on the acoustic match confidence score.
  • the device can be further configured to measure a time gap before or surrounding the audio segment identified via automatic speech recognition as a particular word in a received audio input file or buffer, use a time gap threshold that is specific to the word category of the word, and color code the word as "not correct" and/ or placing the word on a review list if the time gap is greater than the threshold.
  • a computer based method includes receiving audio input associated with a user reading a sequence of words.
  • An automatic speech recognition process can be used to determine if, and at what point in the audio input, the user spoke each word.
  • the sequence of words can be displayed on a graphical user interface, and includes one or more words for which the fluency assessment based on elapsed time is made. For each assessed word there may be a preceding word (the word immediately prior to the assessed word in the text) which is used in the elapsed time measurement.
  • the method also includes determining an approximate amount of time corresponding to an absence of input associated with an assessed word, after receiving audio input identified as the preceding word in the sequence of words.
  • the method also includes determining an approximate amount of time corresponding to an absence of input identified as the assessed word, measured from the start of the audio buffer or file.
  • the method also includes generating a visual intervention by displaying a visual indication on the graphical user interface, if the amount of time is greater than a first threshold.
  • the method includes generating an audio intervention if the amount of time since the visual indication is greater than a second threshold, and audio input that is identified as with the assessed word has still not been received.
  • Embodiments can include one or more of the following.
  • the visual intervention can include a visual indicium applied to the assessed word.
  • the visual indicium can include a visual indicium selected from the group consisting of highlighting the assessed word, underlining the assessed word, or coloring the text of the second word.
  • the method can also include determining an approximate amount of time between receiving audio input that is identified as with the preceding word in the sequence of words and the assessed word in the sequence of words and presenting a visual indicium if the amount of time between receiving audio input that is identified as the preceding word in the sequence of words and the assessed word is greater than a third threshold.
  • the visual indicium can be a deferred visual indicium which is presented after the user has finished the text or indicated to the tutoring software that s/he have stopped reading.
  • the deferred visual indicium can be presented by placing the second word on a review list.
  • the deferred visual indicium can be presented by coloring the text of the assessed word.
  • Generating an audio intervention can include generating an audio intervention selected from the group consisting of an audio file that includes a pronunciation of the assessed word and an audio file that includes an indication for the user to re ⁇ read starting with the assessed word.
  • the method can also include generating a first report including words for which a visual intervention or deferred visual indication was displayed and/or generating a second report including words for which an audio intervention was generated.
  • the first threshold can be between about 1 and 3 seconds.
  • the second threshold can be between about 1 and 8 seconds.
  • the third threshold can be between about 0.5 and 5 seconds.
  • Determining an approximate amount of time between receiving audio input that is identified as a preceding word, and receiving audio input that is identified as an assessed word can include measuring an amount of time from the end of the preceding word to the beginning of the assessed word. Determining an approximate amount of time between receiving audio input identified as a preceding word, and receiving audio input identified as an assessed word, can alternatively include measuring an amount of time from the end of the preceding word to the end of the assessed word.
  • the method can also include adjusting the timing thresholds based on a timing gap between the end of the assessed word and the beginning of the word following the assessed word.
  • the method can also include adjusting the timing thresholds based on the position of the word in the sentence and/or audio file.
  • the method can also include adjusting the timing thresholds based on the proximity of the word to punctuation or a phrase boundary.
  • the timing thresholds can be different for different word categories and for different users of the software.
  • the invention includes a computer program product residing on a computer-readable medium.
  • the computer program product includes instructions for causing an electrical device to receive audio input associated with a user reading a sequence of words.
  • the sequence of words can be displayed on a graphical user interface and can include an assessed word and optionally a word preceding the assessed word.
  • the computer program product can include instructions to determine an approximate amount of time corresponding to an absence of input associated with the assessed word, since receiving audio input identified as the preceding word in the sequence of words, or since the start of the audio file or buffer associated with the sequence of words if there is no preceding word.
  • the computer program product can also include instructions to display a visual intervention on the graphical user interface if the amount of time is greater than a first threshold and subsequent to displaying a visual intervention, generate an audio intervention if the amount of time since the visual indication is greater than a second threshold, and audio input associated with the assessed word has still not been received.
  • Embodiments can include one or more of the following.
  • the visual indicium can include a visual indicium selected from the group consisting of highlighting the assessed word, underlining the assessed word, or coloring the text of the assessed word.
  • the computer program product of can also include instructions to determine an approximate amount of time between the audio segment identified as the preceding word in the sequence of words and the audio segment identified as the assessed word in the sequence of words and present an indicium if the amount of time between the audio segment identified as the preceding word in the sequence of words and the audio segment identified as the assessed word is greater than a third threshold.
  • the computer program product of can also include instructions to generate a first report including words for which a visual intervention or deferred visual indicium was displayed.
  • the computer program product of can also include instructions to generate a second report including words for which an audio intervention was generated.
  • the invention includes a device configured to receive audio input associated with a user reading a sequence of words, the sequence of words displayed on a graphical user interface, and including an assessed word and optionally a word preceding the assessed word.
  • the device is also configured to determine an approximate amount of time corresponding to an absence of input associated with the assessed word, since receiving audio input identified as the preceding word in the sequence of words, or since the start of the audio file or buffer associated with the sequence of words if there is no preceding word.
  • the device is also configured to display a visual intervention on the graphical user interface if the amount of time is greater than a first threshold.
  • the device is also configured to subsequent to displaying a visual intervention, generate an audio intervention if the amount of time since the visual indication is greater than a second threshold, and audio input associated with the assessed word has still not been received.
  • the visual indicium includes a visual indicium selected from the group consisting of highlighting the assessed word, underlining the assessed word, or coloring the text of the assessed word.
  • the device can also be configured to determine an approximate amount of time between the audio segment identified as the preceding word in the sequence of words and the audio segment identified as the assessed word in the sequence of words and present an indicium if the amount of time between the audio segment identified as the preceding word in the sequence of words and the audio segment identified as the assessed word is greater than a third threshold.
  • the device can also be configured to generate a first report including words for which a visual intervention or deferred visual indicium was displayed.
  • the device can also be configured to generate a second report including words for which an audio intervention was generated.
  • a method for interactively tracking oral reading of text from a document includes recording audio for a sentence read by a user and determining when the user has reached the last word of the sentence. The method also includes providing visual feedback to the user reading on a sentence by sentence level to indicate a current location in the passage.
  • Embodiments can include one or more of the following.
  • the method can include determining where the user is within the sentence. Determining where the user is can include determining the current word the user is reading. The method can include assessing the quality of the user's reading on a word-by-word basis. The method can include providing pronunciation and timing indications on a word-by-word basis. Determining where the user is within the sentence can include recognizing the user re-starting reading of the sentence, recognizing the user repeating words, recognizing the user skipping words, and/or recognizing the user skipping reading of sentences or parts thereof.
  • the text sequence that is treated as a sentence by the tutoring software can be a portion of a full syntactic sentence.
  • the sentence-by-sentence visual feedback can be provided by displaying the current sentence text in a first color and the surrounding text in a second color.
  • the sentence after the current sentence can be displayed in a third color.
  • the sentence after the current sentence can be displayed in the same color as the current sentence as the user gets close to the end of the current sentence.
  • the method can include switching a language model to the next sentence when the end of a sentence is reached.
  • the invention includes a computer program product residing on a computer readable medium.
  • the computer program product includes instructions for causing an electrical device to record audio for a sentence read by a user, determine, using speech recognition processing to convert the audio to a text file, when the user has reached the last word of the sentence, and provide visual feedback to the user reading on a sentence by sentence level to indicate a current location in the passage.
  • Embodiments can include one or more of the following.
  • the computer program product can include instructions for causing an electrical device to determine where the user is within the sentence.
  • the computer program can include instructions for causing an electrical device to assess the quality of the user's reading on a word-by-word basis.
  • the computer program product can include instructions for causing an electrical device to provide pronunciation and timing indications on a word-by-word basis.
  • the computer program product can include instructions for causing an electrical device to display the current sentence text in a first color and the surrounding text in a second color.
  • the invention includes a device configured to record audio for a sentence read by a user, determine, using speech recognition processing to convert the audio to a text file, when the user has reached the last word of the sentence, and provide visual feedback to the user reading on a sentence by sentence level to indicate a current location in the passage.
  • Embodiments can include one or more of the following.
  • the device can be configured to determine where the user is within the sentence.
  • the device can be further configured to assess the quality of the user's reading on a word-by-word basis.
  • the device can be further configured to provide pronunciation and timing indications on a word-by-word basis.
  • the device can be further configured to display the current sentence text in a first color and the surrounding text in a second color.
  • a computer based method includes receiving a first portion of audio input associated with a user reading a first portion of a sequence of words prior to a particular word, the sequence of words displayed on a graphical user interface and receiving a second portion of audio input associated with a user reading a second portion of the sequence of words subsequent to the particular word.
  • the method also includes measuring a parameter triggered from the received first portion of audio input, determining if the measured parameter is greater than a threshold, and displaying a visual intervention on the user interface if the parameter is greater than the threshold.
  • Embodiments can include one or more of the following.
  • the threshold can be a time-based threshold.
  • the threshold can be in a range of about 400 to 700 milliseconds.
  • the threshold can be a word count of words in the second portion of the passage.
  • the threshold can in a range of 3-6 words.
  • the method can also include determining an approximate amount of time corresponding to an absence of input since receiving audio input identified as a portion of the sequence of words.
  • the method can also include displaying a visual intervention on the graphical user interface if the amount of time is greater than a second threshold, the second threshold being greater than the first threshold.
  • the method can also include generating an audio intervention if the amount of time since the visual intervention is greater than a third threshold, and audio input associated with the particular word has still not been received. Displaying the visual intervention can include applying a visual indicium to the assessed word.
  • the visual indicium can include a visual indicium selected from the group consisting of highlighting the assessed word, underlining the assessed word, or coloring the text of the assessed word.
  • Applying the visual intervention can include applying a visual indicium to the assessed word after the user has finished the text or has indicated to the tutoring software that he/she has stopped reading.
  • Presenting a deferred indicium can include placing the assessed word on a review list.
  • a computer program product can be tangibly embodied in an information carrier, for executing instructions on a processor.
  • the computer program product can be operable to cause a machine to receive a first portion of audio input associated with a user reading a first portion of a sequence of words prior to a particular word, the sequence of words displayed on a graphical user interface and receive a second portion of audio input associated with a user reading a second portion of the sequence of words subsequent to the particular word.
  • the computer program product can also be operable to cause a machine to measure a parameter triggered from the received first portion of audio input, determine if the measured parameter is greater than a threshold, and display a visual intervention on the user interface if the parameter is greater than the threshold.
  • Embodiments can include one or more of the following.
  • the threshold can be a time- based threshold.
  • the threshold can be a word count of words in the second portion of the passage.
  • a computer based method can include receiving audio input associated with a user reading a sequence of words, the sequence of words displayed on a graphical user interface, and including an assessed word.
  • the method can also include determining an approximate amount of time corresponding to an absence of input associated with the assessed word, since receiving audio input identified as a preceding word in the sequence of words and determining if the assessed word is located near at least one boundary selected from the group consisting of a syntactic boundary or a text layout boundary.
  • the method can also include displaying a visual intervention on the graphical user interface if the amount of time is greater than a first threshold and the assessed word is not located near at least one boundary selected from the group consisting of a syntactic boundary or a text layout and displaying the visual intervention on the graphical user interface if the amount of time is greater than a second threshold and the assessed word is located near at least one boundary selected from the group consisting of a syntactic boundary or a text layout, the second threshold being greater than the first threshold.
  • Embodiments can include one or more of the following.
  • the syntactic boundary can be a punctuation boundary.
  • the syntactic boundary can be a phrase boundary.
  • a computer program product can be tangibly embodied in an information carrier, for executing instructions on a processor. The computer program product can be operable to cause a machine to receive audio input associated with a user reading a sequence of words, the sequence of words displayed on a graphical user interface, and including an assessed word.
  • the computer program product can also be operable to cause a machine to determine an approximate amount of time corresponding to an absence of input associated with the assessed word, since receiving audio input identified as a preceding word in the sequence of words and determine if the assessed word is located near at least one boundary selected from the group consisting of a syntactic boundary or a text layout.
  • the computer program product can also be operable to cause a machine to display a visual intervention on the graphical user interface if the amount of time is greater than a first threshold and the assessed word is not located near at least one boundary selected from the group consisting of a syntactic boundary or a text layout and display the visual intervention on the graphical user interface if the amount of time is greater than a second threshold and the assessed word is located near at least one boundary selected from the group consisting of a syntactic boundary or a text layout, the second threshold being greater than the first threshold.
  • a computer based method can include receiving audio input associated with a user reading a sequence of words, the sequence of words displayed on a graphical user interface, and including an assessed word.
  • the method can also include determining an approximate amount of time corresponding to an absence of input associated with the assessed word, since receiving audio input identified as the preceding word in the sequence of words, determining if the amount of time is greater than a first threshold, and determining if the received audio corresponds to a speech input generated by the user or to silence input.
  • the method can also include, if the received audio corresponds to speech input, setting a delay to a value greater than zero and if the received audio corresponds to silence input, setting the delay to zero.
  • the method can also include displaying a visual intervention on the graphical user interface after the delay, or providing an audio intervention to the user.
  • Embodiments can include one or more of the following.
  • the absence of input associated with the assessed word can include at least one of silence, filler, foil words, or words other than the assessed word.
  • Setting a delay to a value greater than zero can include setting the delay at a value from about 700 milliseconds to about 800 milliseconds.
  • a computer program product can be tangibly embodied in an information carrier, for executing instructions on a processor.
  • the computer program product can be operable to cause a machine to receive audio input associated with a user reading a sequence of words, the sequence of words displayed on a graphical user interface, and including an assessed word.
  • the computer program product can also be operable to cause a machine to determine an approximate amount of time corresponding to an absence of input associated with the assessed word, since receiving audio input identified as the preceding word in the sequence of words, determine if the amount of time is greater than a first threshold, and determine if the received audio corresponds to a speech input generated by the user or to silence input.
  • the computer program product can also be operable to cause a machine to set a delay to a value greater than zero if the received audio corresponds to speech input and set the delay to zero if the received audio corresponds to silence input.
  • the computer program product can also be operable to cause a machine to display a visual intervention on the graphical user interface after the delay.
  • a computer based method includes determining that a visual intervention is needed for an assessed word based on a fluency indication for a user reading a sequence of words displayed on a graphical user interface, storing audio input in a buffer for a predetermined period of time before and during the visual intervention, and displaying the visual intervention on the graphical user interface.
  • the method also includes joining the stored audio from the buffer with audio received subsequent to displaying the visual intervention subsequent to displaying the visual intervention and determining, by evaluating the audio from the buffer joined to the subsequently received audio, if a correct input for the assessed word was received during the visual intervention.
  • Embodiments can include one or more of the following.
  • Determining based on a fluency indication that a visual intervention is needed can include receiving audio input associated with a user reading a sequence of words, the sequence of words displayed on a graphical user interface, and including an assessed word, determining an approximate amount of time corresponding to an absence of input associated with the assessed word, since receiving audio input identified as the preceding word in the sequence of words, and determining if the amount of time is greater than a threshold.
  • the method can also include generating an audio intervention if the amount of time since the visual indication is greater than a second threshold, and audio input associated with the assessed word has still not been received.
  • the visual intervention can include a visual indicium applied to the assessed word.
  • the visual indicium can include a visual indicium selected from the group consisting of highlighting the assessed word, underlining the assessed word, or coloring the text of the assessed word.
  • a computer program product can be tangibly embodied in an information carrier, for executing instructions on a processor.
  • the computer program product can be operable to cause a machine to determine that a visual intervention is needed for an assessed word based on a fluency indication for a user reading a sequence of words displayed on a graphical user interface, store audio input in a buffer for a predetermined period of time before and during the visual intervention, and display the visual intervention on the graphical user interface.
  • the computer program product can also be operable to cause a machine to join the stored audio from the buffer with audio received subsequent to displaying the visual intervention subsequent to displaying the visual intervention.
  • the computer program product can also be operable to cause a machine to determine, by evaluating the audio from the buffer joined to the subsequently received audio, if a correct input for the assessed word was received during the visual intervention.
  • FIG. 1 is a block diagram of a computer system adapted for reading tutoring.
  • FIG.2 is a block diagram of a network of computer systems.
  • FIG. 3 is a screenshot of a passage for use with the reading tutor software.
  • FIG. 4 is a block diagram of inputs and outputs to and from the speech recognition engine or speech recognition process.
  • FIG. 5 is a flow chart of a location tracking process.
  • FIG. 6 is a flow chart of visual and audio interventions.
  • FIG. 7 A and 7B are portions of a flow chart of an intervention process based on elapsed time.
  • FIG. 8 is a screenshot of a set up screen for the tutor software.
  • FIG. 9 is a flow chart of environmental weighting for a word based on a reader's location in a passage.
  • FIG. 10 is a block diagram of word categories.
  • FIG. 11 is a table of exemplary glue words.
  • FIGS. 12A and 12B are portions of a flow chart of a process using word categories to assess fluency.
  • FIG. 13 is a screenshot of a passage.
  • FIG. 14 is a flow chart of an intervention process.
  • FIG. 15 is a flow chart of an intervention process.
  • FIGS. 16A is a flow chart of visual and audio interventions.
  • FIGS. 16B is a block diagram of received audio.
  • FIG. 17 is a flow chart of an intervention timing process.
  • a computer system 10 includes a processor 12, main memory 14, and storage interface 16 all coupled via a system bus 18.
  • the interface 16 interfaces system bus 18 with a disk or storage bus 20 and couples a disk or storage media 22 to the computer system 10.
  • the computer system 10 would also include an optical disc drive or the like coupled to the bus via another interface (not shown).
  • an interface 24 couples a monitor or display device 26 to the system 10.
  • Disk 22 has stored thereon software for execution by a processor 12 using memory 14.
  • an interface 29 couples user devices such as a mouse 29a and a microphone/headset 29b, and can include a keyboard (not shown) to the bus 18.
  • the software includes an operating system 30 that can be any operating system, speech recognition software 32 which can be an open source recognition engine or any engine that provides sufficient access to recognizer functionality, and tutoring software 34 which will be discussed below.
  • speech recognition software 32 which can be an open source recognition engine or any engine that provides sufficient access to recognizer functionality
  • tutoring software 34 which will be discussed below.
  • a user would interact with the computer system principally though mouse 29a and microphone/headset 29b.
  • the arrangement 40 includes multiple ones of the systems 10 or equivalents thereof coupled via a local area network, the Internet, a wide-area network, or an Intranet 42 to a server computer 44.
  • An instructor system 45 similar in construction to the system 10 is coupled to the server 44 to enable an instructor and so forth access to the server 44.
  • the instructor system 45 enables an instructor to import student rosters, set up student accounts, adjust system parameters as necessary for each student, track and review student performance, and optionally, to define awards.
  • the server computer 44 would include amongst other things a file 46 stored, e.g., on storage device 47, which holds aggregated data generated by the computer systems 10 through use by students executing software 34.
  • the files 46 can include text-based results from execution of the tutoring software 34 as will be described below.
  • Also residing on the storage device 47 can be individual speech files resulting from execution of the tutor software 34 on the systems 10.
  • the speech files being rather large in size would reside on the individual systems 10.
  • an instructor can access the text-based files over the server via system 45, and can individually visit a student system 10 to play back audio from the speech files if necessary.
  • the speech files can be selectively uploaded to the server 44.
  • reading depends on an interdependent collection of underlying knowledge, skills, and capabilities.
  • the tutoring software 34 fits into development of reading skills based on existence of interdependent areas such as physical capabilities, sensory processing capabilities, and cognitive, linguistic, and reading skills and knowledge.
  • a person learning to read should also possess basic vocabulary and language knowledge in the language of the text, such as may be acquired through oral language experience or instruction in that language, as well as phonemic awareness and a usable knowledge of phonics.
  • a person should have the physical and emotional capability to sit still and "tune out" distractions and focus on a task at hand. With all of these skills, knowledge, and capabilities in place, a person can begin to learn to read with fluency and comprehension and, through such reading, to acquire the language, vocabulary, information, and ideas of texts.
  • the tutor software 34 described below while useful for students of reading in general, is specifically designed for the user who has developed proper body mechanics and sensory processing and has acquired basic language, alphabet, and phonics skills.
  • the tutor software 34 can develop fluency by supporting frequent and repeated oral reading.
  • the reading tutor software 34 provides this frequent and repeated supported oral reading, using speech recognition technology to listen to the student read and provide help when the student struggles and by presenting records of how much and how accurately and fluently the student has read.
  • the reading tutor software 34 can assist in vocabulary development by providing definitions of words in the built-in dictionary, by keeping track of the user's vocabulary queries, and by providing assistance that may be required to read a text that is more difficult than the user can easily read independently.
  • the tutor software 34 can improve reading comprehension by providing a model reader to which the user can listen, and by assisting with word recognition and vocabulary difficulties.
  • the reading tutor 34 can also improve comprehension by promoting fluency, vocabulary growth, and increased reading. As fluency, vocabulary, and reading experience increase, so does reading comprehension which depends heavily on reading fluency.
  • the software 34 can be used with persons of all ages including children in early though advanced stages of reading development.
  • the tutor software 34 includes passages such as passage 47 that are displayed to a user on a graphical user interface.
  • the passages can include both text and related pictures.
  • the tutor software 34 includes data structures that represent a passage, a book, or other literary work or text.
  • the words in the passage are linked to data structures that store correct pronunciations for the words so that utterances from the user of the words can be evaluated by the tutor software 34.
  • the speech recognition software 32 verifies whether a user's oral reading matches the words in the section of the passage the user is currently reading to determine a user ' s level of fluency.
  • the speech recognition engine 32 in combination with the tutor software 34 analyzes speech or audio input 50 from the user, and generates a speech recognition result 66.
  • the speech recognition engine 32 uses an acoustic model 52, a language model 64, and a pronunciation dictionary 70 to generate the speech recognition result 66.
  • the acoustic model 52 represents the sounds of speech (e.g., phonemes). Due to differences in speech for different groups of people or individual users, the speech recognition engine 32 includes multiple user acoustic models 52 such as an adult male acoustic model 54, an adult female acoustic model 56, a child acoustic model 58, and a custom acoustic model 60. In addition, although not shown in FIG. 4, acoustic models for various regional accents, various ethnic groups, or acoustic models representing the speech of users for which English is a second language could be included. A particular one of the acoustic models 52 is used to process audio input 50, identify acoustic content of the audio input 50, and convert the audio input 50 to sequences of phonemes 62 or sequences of words 68.
  • a particular one of the acoustic models 52 is used to process audio input 50, identify acoustic content of the audio input 50, and convert the audio input 50 to sequences of phonemes 62 or
  • the pronunciation dictionary 70 is based on words 68 and phonetic representations.
  • the words 68 come from the story texts or passages, and the phonetic representations 72 are generated based on human speech input or models.
  • Both the pronunciation dictionary 70 and the language model 64 are derived from the story texts to be recognized.
  • the words are taken independently from the story texts.
  • the language model 64 is based on sequences of words from the story texts or passages.
  • the recognizer uses the language model 64 and the pronunciation dictionary 70 to constrain the recognition search and determine what is considered from the acoustic model when processing the audio input from the user 50.
  • the speech recognition process 32 uses the acoustic model 52, a language model 64, and a pronunciation dictionary 70 to generate the speech recognition result 66.
  • a process 80 for tracking a user's progress through the text and providing feedback to the user about the current reading location in a passage is shown.
  • the tutor software 34 guides the student through the passage on a sentence-by-sentence basis using sentence-by-sentence tracking, hi order to provide sentence-by-sentence tracking, a passage is displayed 82 to the user.
  • the sentence-by-sentence tracking provides 84 a visual indication (e.g., changes the color of the words, italicizes, etc.) for an entire sentence to be read by the user.
  • the user reads the visually indicated portion and the system receives 86 the audio input.
  • the system determines 88 if a correct reading of the indicated portion has been received.
  • the portion remains visually indicated 90 until the speech recognition obtains an acceptable recognition from the user.
  • the visual indication progresses 92 to a subsequent (e.g., the next) sentence or clause.
  • the visual indication may progress to the next sentence before the user completes the current sentence, e.g. when the user reaches a predefined point in the first sentence.
  • Sentence-by-sentence tracking can provide advantages over word-by ⁇ word tracking (e.g., visually indicating only the current word to be read by the user, or 'turning off the visual indication for each word as soon as it has been read correctly).
  • Word-by-word tracking may be more appropriate in some situations, e.g., for users who are just beginning to learn to read.
  • sentence-by-sentence tracking can be particularly advantageous for users who have mastered a basic level of reading and who are in need of developing reading fluency and comprehension.
  • Sentence-by-sentence tracking promotes fluency by encouraging students to read at a natural pace without the distraction of having a visual indication change with every word. For example, if a child knows a word and can quickly read a succession of multiple words, word-by-word tracking may encourage the user to slow his or her reading because the words may not be visually indicated at the same rate as the student would naturally read the succession of words.
  • Sentence-by-sentence feedback minimizes the distraction to the user while still providing guidance as to where s/he should be reading within the passage.
  • sentence transitions or clause transitions are indicated in the software's representation of the passage. These transitions can be used to switch the recognition context (language model) and provide visual feedback to the user.
  • the tracking process 80 aligns the recognition result to the expected text, taking into account rules about what words the tutor software recognizes and what words can be skipped or misrecognized (as described below).
  • the tutor software 34 is described as providing visual feedback based on a sentence level, other segmentations of the passage are possible and can be treated by the system as sentences.
  • the tutor software can provide the visual indication on a phrase-by- phrase basis, a clause-by-clause basis, or a line-by-line basis.
  • the line-by-line segmentation can be particularly advantageous for poetry passages. Phrase-by-phrase and clause-by-clause segmentation can be advantageous in helping the student to process the structure of long and complex sentences.
  • a visual indication is also included to distinguish the portions previously read by the user from the portions not yet completed.
  • the previously read portions could be displayed in a different color or could be grayed. The difference in visual appearance of the previously read portions can be less distracting for the user and help the user to easily track the location on the screen.
  • the highlighting can shift as the user progresses in addition to changing or updating the highlighting or visual indication after the recognition of the completion of the sentence. For example, when the user reaches a predetermined transition point within one sentence the visual indication may be switched off for the completed part of that sentence and some or all of the following sentence may be indicated.
  • the system tracks where the user is on a word-by-word basis.
  • the location is tracked on a word-by-word basis to allow the generation of interventions.
  • interventions are processes by which the application assists a user when the user is struggling with a particular word in a passage. It also tracks on a word-by- word basis so as to allow evaluation, monitoring and record-keeping of reading accuracy and fluency, and to generate reports to students and teachers about same.
  • the tutor software 34 provides multiple levels of interventions, for example, the software can include a visual intervention state and audio intervention state, as shown in FIG. 6.
  • the tutor software 34 When the tutor software 34 does not receive a valid recognition on an expected word after a specified duration has elapsed, the tutor software 34 intervenes 106 by applying a visual indication to the expected word. For example, a yellow or other highlight color may be applied over title word. Words in the current sentence that are before the expected word may also be turned from black to gray to enable the user to quickly identify where he/she should be reading. The user is given a chance to self-correct or re-read the word. The unobtrusive nature of the visual intervention serves as a warning to the student without causing a significant break in fluent reading. If the tutor software 34 still fails 108 to receive an acceptable recognition of the word, an audio intervention takes place 110.
  • a visual indication For example, a yellow or other highlight color may be applied over title word. Words in the current sentence that are before the expected word may also be turned from black to gray to enable the user to quickly identify where he/she should be reading. The user is given a chance to self-correct or
  • a recording or a synthesized version of the word plays with the correct pronunciation of the word and the word is placed 114 on a review list.
  • a recording indicating "read from here" may be played, particularly if the word category 190 indicates that the word is a short common word that the user is likely to know. In this case, the user is likely struggling with a subsequent, more difficult word or is engaged in extraneous vocalization, so likewise the software may not place the word on a review list depending on the word category (e.g. if the word is a glue word 194).
  • the tutor software 34 gives the student the opportunity to re-read the word correctly and continue with the current sentence.
  • the tutor software 34 determines if a valid recognition for the word has been received and if so, proceeds 102 to a subsequent word, e.g., next word. If a valid recognition is not received, the software will proceed to the subsequent word after a specified amount of time has elapsed.
  • the reading tutor software 34 provides visual feedback to the user on a sentence-by-sentence basis as the user is reading the text (e.g. the sentence s/he is currently reading will be black and the surrounding text will be gray).
  • This user interface approach minimizes distraction to the user compared to providing feedback on a word-by-word basis (e.g., having words turn from black to gray as s/he is recognized).
  • the sentence-by-sentence feedback approach it can be desirable to non-disraptively inform the user of the exact word (as opposed to sentence) where the tutor software expects the user to be reading.
  • the software may need to resynchronize with the user due to several reasons.
  • the user may have read a word but questioned or slurred the word and the word was not recognized, the application may have simply misrecognized a word, the user may have lost his/her place in the sentence, the user may have said something other than the word, and the like. It can be preferable to provide an intervention to help to correct such errors, but a full intervention that plays the audio for the word and marks the word as incorrect and puts the word on the review list may not be necessary. Thus, a visual intervention allows the user or the application to get back in synchronization without the interruption, distraction, and/or penalty of a full intervention on the word.
  • the tutor software 34 can provide an intervention based on the length of time elapsed since the previous word, or since the start of the audio buffer or file, during which the tutor software 34 has not yet received a valid recognition for the expected word.
  • Process 130 includes initializing 132 a timer, e.g., a software timer or a hardware timer can be used.
  • the timer can be initialized based on the start of a silence (no voice input) period, the start of a new audio buffer or file, the completion of a previous word, or another audio indication.
  • the timer determines 136 a length of time elapsed since the start of the timer.
  • Process 130 determines 140 if the amount of time on the timer since the previous word is greater than a threshold. If the time is not greater than the threshold, process 130 determines 138 if valid recognition has been received.
  • process 130 returns to determining the amount of time that has passed. This loop is repeated until either a valid recognition is received or the time exceeds the threshold. If a valid recognition is received (in response to determination 138), process 130 proceeds 134 to a subsequent word in the passage and re-initializes 132 the timer. If the time exceeds the threshold, process 130 provides 142 a first / visual intervention. For example, the tutor software highlights the word, changes the color of the word, underlines the word, etc.
  • process 130 determines 144 an amount of time since the intervention or a total time. Similar to the portion of the process above, process 130 determines 148 if the amount of time on the timer is greater than a threshold. This threshold may be the same or different than the threshold used to determine if a visual intervention is needed. If the time is not greater than the threshold, process 130 determines 150 if a valid recognition has been received. If input has not been received, process 130 returns to determining 148 the amount of time that has passed. This loop is repeated until either a valid recognition is received or the time exceeds the threshold. If a valid recognition is received (in response to determination 148), process 130 proceeds 146 to a subsequent word in the passage and re-initializes 132 the timer. If the time exceeds the threshold, process 130 provides 152 an audio intervention.
  • process 130 determines 156 an amount of time since the intervention or a total time and determines 148 if the amount of time is greater than a threshold (e.g., a third threshold). This threshold may be the same or different from the threshold used to determine if a visual intervention or audio intervention is needed. If the time is not greater than the threshold, process 130 determines 158 if a valid recognition has been received. If input has not been received, process 130 returns to determining 160 the amount of time that has passed. This loop is repeated until either a valid recognition is received or the time exceeds the threshold. If a valid recognition is received (in response to determination 160), process 130 proceeds 154 to a subsequent word in the passage and re-initializes 132 the timer. If the time exceeds the threshold, process 130 proceeds 162 to a subsequent word in the passage, but the word is indicated as not receiving a correct response within the allowable time period.
  • a threshold e.g., a third threshold. This threshold may be the same or different from the threshold used to determine
  • the visual intervention state and the full audio intervention state are used in combination.
  • a visual intervention is triggered after a time-period has elapsed in which the tutor software 34 does not recognize a new sentence word.
  • the "visual intervention interval" time period can be about 1-3 seconds, e.g., 2 seconds as used in the example below. However, the interval can be changed in the application's configuration settings (as shown in FIG. 8). For example, if the sentence is "The cat sat" and the tutor software 34 receives a recognition for the word "The”, e.g., 0.9 seconds from the time the user starts the sentence, no intervention will be triggered for the word "The” since the time before receiving the input is less than the set time period.
  • the tutor software 34 triggers a visual intervention on the word "cat”" (the first sentence word that has not been recognized).
  • words in the current sentence which are prior to the intervened word are colored gray.
  • the word that triggered the visual intervention e.g. cat
  • the remainder of the sentence is black.
  • Other visual representations could, however, be used.
  • a new recording starts with the visually intervened word and the tutor software re-synchronizes the recognition context (language model) so that the recognizer expects an utterance beginning with the intervened word.
  • the intervened word is coded, e.g., green, or correct unless the word is a member of a certain word category. For example if the word is a target word, it can be coded in a different color, and/or placed on a review list, indicating that the word warrants review even though it did not receive a full audio intervention. If the user does not read the word successfully, a full audio intervention will be triggered after a time period has elapsed. This time period is equal to the Intervention Interval (set on a slider in the application, e.g., as shown in FIG. 8) minus the visual intervention interval.
  • Intervention Interval set on a slider in the application, e.g., as shown in FIG. 8
  • the time periods before the visual intervention and between the visual intervention and the full intervention would be a minimum of about 1- 5 seconds so that these events do not trigger before the user has been given a chance to say a complete word.
  • the optimum time period settings will depend upon factors including the reading level of the text, the word category, and the reading level, age, and reading rate of the user. If the Intervention Interval is set too low (i.e. at a value which is less than the sum of the minimum time period before the visual intervention, and the minimum time period between the visual intervention and the full intervention), the visual intervention state will not be used and the first intervention will be an audio intervention.
  • the speech recognition screen 170 allows a user or administrator to select a particular user (e.g., using selection boxes 171) and set speech recognition characteristics for the user.
  • the user or administrator can select an acoustic model by choosing between acoustic models included in the system by selecting one of the acoustic model boxes 172.
  • the user can select a level of pronunciation correctness using pronunciation correctness continuum or slider 173.
  • the use of a pronunciation correctness slider 173 allows the level of accuracy in pronunciation to be adjusted according to the skill level of the user.
  • the user can select an intervention delay using intervention delay slider 174.
  • the intervention delay slider 174 allows a user to select an amount of time allowed before an intervention is generated.
  • speech recognition is used for tracking where the user is reading in the text. Based on the location in the text, the tutor software 34 provides a visual indication of the location within the passage where the user should be reading.
  • the speech recognition can be used in combination with the determination of interventions to assess at what rate the user is reading and to assess if the user is having problems reading a word, hi order to maximize speech recognition performance, the tutor software dynamically defines a "recognition configuration" for each utterance (i.e. audio file or buffer that is processed by the recognizer). A new utterance will be started when the user starts a new sentence or after a visual intervention or audio intervention.
  • the recognition configuration includes the set of items that can be recognized for that utterance, as well as the relative weighting of these items in the recognizer's search process.
  • the search process may include a comparison of the audio to acoustic models for all items in the currently active set.
  • the set of items that can be recognized may include expected words, for example, the words in the current sentence, words in the previous sentence, words in the subsequent sentence, or words in other sentences in the text.
  • the set of items that can be recognized may also include word competition models. Word competition models are sequences of phonemes derived from the word pronunciation but with one or more phonemes omitted, or common mispronunciations or mis-readings of words.
  • the set of recognized sounds include phoneme fillers representing individual speech sounds, noise fillers representing filled pauses (e.g. "urn") and non-speech sounds (e.g. breath noise).
  • the relative weighting of these items is independent of prior context (independent of what has already been recognized in the current utterance, and of where the user started in the text).
  • the relative weighting of items is context-dependent, i.e. dependent on what was recognized previously in the utterance and/or on where the user was in the text when the utterance started.
  • the context-dependent weighting of recognition items is accomplished through language models.
  • the language models define the words and competition models that can be recognized in the current utterance, and the preferred (more highly weighted) orderings of these items, in the recognition sequence.
  • the language model 64 defines the items (unigrams - a single word), ordered pairs of items (bigrams - a two word sequence), and ordered triplets of items (trigrams - a three word sequence) to be used by the recognition search process. It also defines the relative weights of the unigrams, bigrams, and trigrams which is used in the recognition search process. Additionally, the language model defines the weights to be applied when recognizing a sequence (bigram or trigram) that is not explicitly in the language model. However, unlike a statistical language model, the language model 64 is not based on statistics derived from large amounts of text. Instead it is based on the sequence of words in the text and on patterns of deviation from the text that are common among readers.
  • the language model generation process 177 takes the current text 178 that the user is reading and divides it into segments 179.
  • each segment includes the words in a single sentence and one or more words from the following sentence.
  • the segment could be based on other units such as paragraph, a page of text, or a phrase.
  • the unigram, bigram, and trigram word sequences and corresponding weights are defined 180 based on the sequence of words in the sentence, and the word competition models for those words.
  • the language model generation process uses rules about which words in the sentence maybe skipped or not recognized in oral reading (based on word category).
  • the speech recognition process selects the language model to use based on where the user is reading in the text 186 (e.g., the process selects the language model for the current sentence).
  • the recognition process adjusts the probability or score of recognition alternatives currently being considered in the recognition search based on the language model 185.
  • the "prior context" used by the language model to determine weightings comes from recognition alternatives for the utterance up until that point. For example, if the sentence is "The cat sat on the mat" and a recognition alternative for the first part of the utterance is "The cat", then the weightings provided by the language model will typically prefer a recognition for "sat" as the next word over other words in the sentence.
  • the tutor software uses the prior context based on where the user was in the text at the start of this utterance.
  • This "initial recognition context” information is also included in the language model. Therefore, if the user just received an intervention on "sat” and is therefore starting an utterance with that word, the initial recognition context of "the cat” (the preceding text words) will mean that the weightings applied will prefer recognition for "sat” as the first word of the utterance.
  • the language model 64 is sentence-based and is switched dynamically 186 each time the user enters a new sentence.
  • the "initial recognition context” is based on the precise point in the text where the current utterance was started.
  • the "pronunciation correctness slider” can control many aspects of the relative weighting of recognition items, as well as the content of the language model, and this setting can be changed either by the user or by the teacher during operation.
  • Weightings or other aspects of recognition configuration that can be controlled include the relative weighting of sequences including word competition models in the language model, the relative weighting of word sequences which are explicitly in the language model (represented in bigrams and trigrams) vs. sequences which are not, and the content of the language model.
  • the content of the language model is chosen based on how competition models are generated, what word sequences are explicitly in the language model and how s/he are weighted relative to one another.
  • the "pronunciation correctness slider" setting may also control the relative weighting of silence, noise, or phoneme filler sequences vs. other recognition items.
  • the language model includes the words in the current sentence and one or more words from the subsequent sentence (up to and including the first non- glue word in the subsequent sentence).
  • the subsequent sentence words are included to help the tutor software 34 determine when the user has transitioned from the current sentence into the next sentence, especially in cases where the reader does not pause between sentences.
  • a set of word classifications or categories 190 is shown.
  • the word categories can have different settings in the speech recognition and tutor software 34. The settings can be used to focus on particular words or sets of words in a passage.
  • Word categories 190 include target words 192, glue words 194, and other words 196. Words in a passage or story are segmented into one or more of these categories or other word categories according to his or her type as described below. Based on the category, the acoustic match confidence score may be used to determine the color coding of the word and whether the word is placed on a review list.
  • Glue words 194 include common words that are expected to be known by the student or reader at a particular level.
  • the glue words 194 can include prepositions, articles, pronouns, helping verbs, conjunctions, and other standard/common words.
  • a list of common glue words 194 is shown in FIG. 11. Since the glue words 194 are expected to be very familiar to the student, the tutor software and speech recognition engine may not require a strict acoustic match confidence on the glue words 194. In some examples, the software may not require any recognition for the glue words 194.
  • the relaxed or lenient treatment of glue words 194 allows the reader to focus on the passage and not be penalized or interrupted by an intervention if a glue word is read quickly, indistinctly, or skipped entirely.
  • Target words 192 also can be treated differently than other words in the passage.
  • Target words 192 are the words that add content to the story or are the new vocabulary for a passage. Since the target words are key words in the passage, the acoustic match confidence required for the target words 192 can be greater than for non-target words. Also, the word competition models may be constructed or weighted differently for target words. In addition, the target words 192 maybe further divided into multiple sub-classifications, each sub-classification requiring different treatment by the speech recognizer and the tutoring software.
  • Additional word categories may also be defined, such as a category consisting of words which the user has mastered based on the user's past reading history.
  • the time gap measurement may not be used to color code words or place words on the review list if the words are in the mastered word category. Instead, if the time gap measurement for the mastered word exceeds a threshold, it will be used as an indication that the user struggled with a different word in the sentence or with the overall interpretation of the sentence.
  • Words in a text can be assigned to a word category based on word lists. For example, words can be assigned to the glue word category if the are on a list such as the common glue word list (FIG. 11), assigned to the mastered word category if s/he are on a list of words already mastered by that user, and assigned to a target word category if s/he are in a glossary of new vocabulary for a passage.
  • word categorization can also take into account additional factors such as the importance of a word to the meaning of a particular sentence, the lesson focus, and the reading level of the user and of the text. Therefore a word may be assigned to a particular category (e.g. the glue word category) in one sentence or instance, and the same word may assigned to a different category in another sentence or instance, even within the same text.
  • a process 200 related to the progression of a reader through a story is shown.
  • the speech recognition software determines 202 the word category for the next or subsequent word in the passage.
  • the speech recognition software determines 204 if the word is a target word.
  • the speech recognition software 32 receives 208 audio from the user and generates a recognition sequence corresponding to the audio. If a valid recognition for an expected word is not received, the software will follow the intervention processes outlined above, unless the word is a glue word. If the word is a glue word, a valid recognition may not be required for the word. In this example, the speech recognition software receives 210 audio input including the expected glue word or a subsequent word and proceeds 216 to a subsequent word.
  • the tutor software analyzes additional information obtained from the speech recognition sequence.
  • the software measures 222 and 224 if there was a time gap exceeding a predetermined length prior to or surrounding the expected word. If there is such a time gap, the word is placed 220 on a review list and coded a color to indicate that it was not read fluently. Typically this color is a different color from that used for 'correct' words (e.g. green), and also different from the color used to code words that have received an audio intervention (e.g. red).
  • the software analyzes the acoustic match confidence 214 that has been generated for the word.
  • the acoustic match confidence is used to determine if the audio received from the user matches the expected input (as represented by the acoustic model for that word) closely enough to be considered as a correct pronunciation.
  • the speech recognition software determines 218 if the acoustic match confidence for the particular target word is above a predefined level. If the match confidence is not above the level, the word is placed on a review list 220 and coded a color to indicate that it was not read correctly or fluently. After determining the coding of the word, the tutor software 34 proceeds 226 to the subsequent word.
  • word categories may include additional different treatment of words and may include more or fewer word categories 190.
  • the treatment of different categories of words can be controlled dynamically at the time the software is run.
  • the tutor software 34 generates a list of review words based on the student's reading of the passage. A word may also be placed on the review list for reasons not directly related to the student's reading of the passage, for example if the student requested a definition of the word from the tutor software, the word could be placed on the review list.
  • the review list can include one or more classifications of words on the review list and words can be placed onto the review list for multiple reasons.
  • the review list can be beneficial to the student or to an administrator or teacher for providing feedback related to the level of fluency and specific difficulties for a particular passage.
  • the review list can be used in addition to other fluency assessment indications such as number of total interventions per passage or words per minute.
  • the list of review words can be color-coded (or distinguished using another visual indication such as a table) based on the reason the word was included in the review list.
  • words can be included in the review list if an acoustic match confidence for the word was below a set value or if the user struggled to say the word (e.g., there was a long pause prior to the word). Words can also be placed on the review list if the user received a full audio intervention for the word (e.g., if the tutor software did not receive a valid recognition for the word in a set time, or the user requested an audio intervention for that word).
  • Words that have been included on the review list due an audio intervention can be color coded in a one color while words placed on the review list based on the analysis of a valid recognition for the word (either time gaps associated with the word, or acoustic match confidence measurements) can be color coded in a second color.
  • the words can also be color coded directly in the passage as the student is reading the passage.
  • the word 234 'huge' is coded in a different manner than the word 236 'wolf.
  • the first color-coding on word 234 is related to a pause exhibited in the audio input between the word 'what' and the word 'huge'.
  • the second color-coding on word 236 is related to the user receiving an audio intervention for the word 236. Both words 234 and 236 would also be included on a list of review words for the user.
  • the language models and sentence tracking have been described above based on a sentence, other division points within a passage could be used.
  • the language models and sentence-by-sentence tracking could be applied to sentence fragments as well as to complete sentences.
  • s/he could use phrases or lines as the "sentence.”
  • line-by-line type sentence-by-sentence tracking can be useful to promote fluency in poetry reading.
  • tracking sentences by clauses or phrases can allow long sentences to be divided and understood in more manageable linguistic units by the user.
  • single words may be used as the unit of tracking.
  • the unit of tracking and visual feedback need not be the same as the unit of text used for creating the language models.
  • the language models could be based on a complete sentence whereas the tracking could be phrase-by-phrase or word-by-word.
  • Process 250 begins 251 counters and so forth.
  • Process 250 receives input from the user and starts 254 a software based timer.
  • the software based timer is started after a correctly received word and measures 256 the duration (e.g., a number of milliseconds) of "continuation speech" after the next expected word.
  • Continuation speech includes speech input generated by the user and recognized by the software to match words in the text that occur subsequent to a word for which there was a potential error.
  • the timer is started after each correctly received word and the next expected word is assumed to be a potential "error.” Therefore, the software looks for continuation speech after each correctly received word.
  • the timer does not count the total elapsed time, but instead counts the time (e.g., in milliseconds) of matched "continuation speech". For example, if the recognized word sequence was "A car sat in the ⁇ pause> mat" and the expected text sequence was "A cat sat on the mat" only the duration of sat, the, and mat would count towards the continuation speech measurement to be compared to the threshold in the case where the next expected word is "cat". The time elapsed while the user is speaking incorrect words or when the user pauses is not counted as continuation speech by the timer.
  • the timer counts from the last correctly recognized word in a sentence, e.g., the last word before the word for which a successful recognition has not yet been received.
  • the recognition of continuation speech is determined by extending the alignment process past the about-to-be intervened or expected word to include words in the text subsequent to the expected word.
  • the alignment process determines how the sequence of recognized words in the input audio match to the sequence of expected text words.
  • Process 250 includes determining 258 if the measured time is greater than a threshold.
  • the threshold can be set as desired.
  • the threshold can be a length of time from about 500 milliseconds to about 700 milliseconds (e.g., 600 milliseconds). Longer thresholds can be used as desired.
  • the threshold could be a length of time from about 700 milliseconds to 5 seconds.
  • process 250 provides 262 an accelerated intervention. If the measured time is not greater than the threshold, process 260 continues to increment the total time of continuation speech and determines 260 if a correct recognition for the next expected word (potential error word) has been received. If a correct recognition is received, process 250 ends 264 counting the continuation text for that word. If a correct recognition is not received, process 250 returns to determining 258 if the time is greater than the threshold.
  • accelerated interventions as described in relation to FIG. 14 allows the software to alert the reader of a skipped or incorrectly pronounced word when the reader has continued reading a portion of the passage subsequent to the skipped or incorrectly pronounced word. Accelerating the intervention allows the intervention to be provided before the user progresses too far past to the error word in the text, hi general, the amount of time used as a threshold for the accelerated intervention is less than the amount of time used for providing a visual intervention when the user has not continued reading a subsequent portion of the text.
  • Process 280 initializes 251 counters etc. and is similar to process 250 shown in FIG. 14. However, while process 250 provides an accelerated intervention based on a time measurement, process 280 provides an accelerated intervention based on word count.
  • Process 280 includes counting 286 the number of words of "continuation speech" after the expected word.
  • Process 280 determines 288 if the count of the number of words is greater than a threshold.
  • the threshold is configurable. For example, the threshold can be any number or words from, e.g., about two words to about five or six words (e.g., three words).
  • process 280 If the count of the number of words is greater than or equal to the threshold, process 280 provides 292 an accelerated intervention. If the number of words is not greater than a threshold, process 280 continues to increment the word count for additional received continuation speech and determines 290 if a correct recognition for the error word is received. If a correct recognition is received, process 280 ends 294. If a correct recognition is not received, process 280 returns to determining 288 if the number of words of continuation speech is greater than the threshold.
  • a user may speak a word in about the same timeframe as a pre- intervention (e.g., a visual intervention) is triggered. This can result in a false negative for the reader because the reader has correctly spoken the word, but the software has not recognized the word.
  • Such a false negative can occur because the user is still in the process of saying the word when the pre-intervention is triggered, e.g. by a timer. It can also be due to various sources of delay that exist in the system.
  • a fixed amount of audio prior to a pre- intervention is saved and re-used at the start of the new utterance after the intervention.
  • the audio input can be re-joined with truncated audio for a word, hi this case, the pre-intervention does occur, but combining the overlapped audio from before the pre-intervention to the audio immediately after the pre-intervention enables the system to correctly recognize the word and avoid providing a full intervention (e.g., an audio intervention) on the word.
  • Process 300 includes continuously buffering 302 audio received from the user.
  • Process 300 includes providing 304 a visual intervention to the user (e.g., as described above). After providing the visual intervention, process 300 rejoins 306 the stored audio in the buffer file with the received audio and compares 308 the rejoined audio to the expected audio, as discussed in FIG. 16B below.
  • Process 300 determines 310 if the re-joined audio provides audio input that corresponds to valid recognized speech. If valid recognized speech is included in the re-joined audio, process 300 proceeds 312 to a subsequent word or portion of the passage. If valid recognized speech was not received, process 300 determines 314 if an audio intervention is needed (e.g., as described above).
  • FIG. 16B a block diagram of stored audio 328 and received audio 331 is shown.
  • the system prior to providing a visual intervention (indicated by line 327) the system begins recording audio input from the input. The recorded audio is stored in a buffer file 328.
  • the system begins receiving and analyzing the audio 331 received after the visual intervention 329.
  • the system joins the audio buffer file 328 with the audio received after the intervention 331 and determines if the combination of the audio in the buffer file 328 and the audio received after the intervention 331 includes audio input that corresponds to valid recognized speech.
  • Process 330 includes determining 332 that a pre- intervention (e.g., a visual intervention), or audio intervention, is needed. For example, the determination can be based on a timer that counts a length of time since a valid recognition. After determining 332 that an intervention is needed, process 330 determines 334 if the most recent audio input result ends with a recognition unit corresponding to speech (e.g., filler, foil, or word). If the most recent recognition does not correspond to speech input (e.g., the most recent recognition includes silence), then process 330 provides 336 the intervention.
  • a pre- intervention e.g., a visual intervention
  • audio intervention e.g., a visual intervention
  • the determination can be based on a timer that counts a length of time since a valid recognition.
  • process 330 determines 334 if the most recent audio input result ends with a recognition unit corresponding to speech (e.g., filler, foil, or word). If the most recent recognition does not correspond to speech input (e
  • process 330 defers 338 the intervention for a fixed time period (e.g., 700 to 800 milliseconds). Deferring the intervention allows a reader time to finish pronouncing a word. After the fixed time period, process 330 determines 340 if a correct recognition was received for the word. If a correct recognition was received, process 330 proceeds 342 to a subsequent word or portion of the passage. If a correct recognition was not received, process 330 provides 344 the intervention.
  • a fixed time period e.g. 700 to 800 milliseconds.
  • the threshold for receiving a visual or audio input was constant the threshold can vary dependent on the user's location within the text. For example, the threshold for triggering a visual or audio intervention can be greater when the user is at a boundary in the text.
  • boundaries can include syntactic boundaries such as sentence boundaries, clause boundaries, or other punctuation based boundaries and text layout boundaries such as the end of a line, end of paragraph, or the end of a page.
  • the system can provide support to people who are learning to read a second language.
  • the system can support people who are learning to read in a language other than English, whether as a first or second language.
  • the system can have a built-in dictionary that will explain a word's meaning as it is used in the text.
  • the built-in dictionary can provide information about a word's meaning and usage in more than one language including, for example, the language of the text and the primary language of the user. Accordingly, other embodiments are within the scope of the following claims.

Abstract

L'invention concerne des procédés et des produits-programmes, des systèmes et des dispositifs informatiques associés de retour d'information intelligent à un utilisateur, basé sur une entrée audio associée à la lecture d'un passage par l'utilisateur. Ledit procédé consiste à attribuer à un utilisateur un niveau de capacité à la lecture d'une séquence de mots par technologie de reconnaissance vocale, afin de comparer l'entrée audio avec une séquence de mots prévue et fournir un retour d'information à l'utilisateur concernant son niveau de capacité pour un mot.
PCT/US2005/031769 2004-09-10 2005-09-08 Retour d'information de tutorat intelligent WO2006031536A2 (fr)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US10/938,762 2004-09-10
US10/938,746 2004-09-10
US10/939,295 US20060069562A1 (en) 2004-09-10 2004-09-10 Word categories
US10/938,762 US9520068B2 (en) 2004-09-10 2004-09-10 Sentence level analysis in a reading tutor
US10/938,748 2004-09-10
US10/939,295 2004-09-10
US10/938,748 US7433819B2 (en) 2004-09-10 2004-09-10 Assessing fluency based on elapsed time
US10/938,746 US8109765B2 (en) 2004-09-10 2004-09-10 Intelligent tutoring feedback

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EP3544001A1 (fr) * 2018-03-23 2019-09-25 Articulate.XYZ Ltd Traitement de transcriptions parole-texte
WO2020014730A1 (fr) * 2018-07-16 2020-01-23 Bookbot Pty Ltd Aide à l'apprentissage

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Cited By (11)

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Publication number Priority date Publication date Assignee Title
RU2474386C2 (ru) * 2007-06-01 2013-02-10 Конинклейке Филипс Электроникс, Н.В. Кабель беспроводного ультразвукового зонда
RU2502470C2 (ru) * 2007-06-01 2013-12-27 Конинклейке Филипс Электроникс, Н.В. Облегченный беспроводной ультразвуковой датчик
KR20160025301A (ko) * 2014-08-27 2016-03-08 삼성전자주식회사 음성 인식이 가능한 디스플레이 장치 및 방법
KR102357321B1 (ko) 2014-08-27 2022-02-03 삼성전자주식회사 음성 인식이 가능한 디스플레이 장치 및 방법
EP3544001A1 (fr) * 2018-03-23 2019-09-25 Articulate.XYZ Ltd Traitement de transcriptions parole-texte
WO2019179884A1 (fr) * 2018-03-23 2019-09-26 Articulate.Xyz Limited Traitement de transcriptions de parole en texte
CN111971744A (zh) * 2018-03-23 2020-11-20 清晰Xyz有限公司 处理语音到文本的转换
US20210027786A1 (en) * 2018-03-23 2021-01-28 Articulate.Xyz Limited Processing speech-to-text transcriptions
US11790917B2 (en) 2018-03-23 2023-10-17 Articulate.XYZ Ltd Processing speech-to-text transcriptions
WO2020014730A1 (fr) * 2018-07-16 2020-01-23 Bookbot Pty Ltd Aide à l'apprentissage
CN112567456A (zh) * 2018-07-16 2021-03-26 万卷智能有限公司 学习辅助工具

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