EP1668631A1 - Identifying natural speech pauses in a text string - Google Patents

Identifying natural speech pauses in a text string

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Publication number
EP1668631A1
EP1668631A1 EP04784433A EP04784433A EP1668631A1 EP 1668631 A1 EP1668631 A1 EP 1668631A1 EP 04784433 A EP04784433 A EP 04784433A EP 04784433 A EP04784433 A EP 04784433A EP 1668631 A1 EP1668631 A1 EP 1668631A1
Authority
EP
European Patent Office
Prior art keywords
word
text string
pauses
threshold value
speech
Prior art date
Legal status (The legal status 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 status listed.)
Withdrawn
Application number
EP04784433A
Other languages
German (de)
French (fr)
Other versions
EP1668631A4 (en
Inventor
Gui-Lin Chen
Yi-Qing Zu
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Motorola Solutions Inc
Original Assignee
Motorola 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
Application filed by Motorola Inc filed Critical Motorola Inc
Publication of EP1668631A1 publication Critical patent/EP1668631A1/en
Publication of EP1668631A4 publication Critical patent/EP1668631A4/en
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/08—Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
    • G10L13/10—Prosody rules derived from text; Stress or intonation
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/08—Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/06—Elementary speech units used in speech synthesisers; Concatenation rules

Definitions

  • the present invention relates generally to Text-To-Speech (TTS) synthesis.
  • TTS Text-To-Speech
  • the invention is particularly useful for determining natural pauses in synthesized pronunciation of a text segment.
  • TTS Text to Speech
  • concatenated text to speech synthesis allows electronic devices to receive an input text string .and provide a converted representation of the string in the form of synthesized speech.
  • a device that may be required to synthesize speech originating from a non-deterministic number of received text strings will have difficulty in providing high quality realistic synthesized speech. That is because the pronunciation of each word or syllable (for Chinese characters and the like) to be synthesized is context and location dependent. For example, a pronunciation of a word at the end of a sentence (input text string) may be drawn out or lengthened.
  • the pronunciation of the same word may be lengthened even more if it occurs in the middle of a sentence where emphasis is required at a natural speech pause.
  • the pronunciation of a word depends on acoustic prosodic parameters comprising tone (pitch), volume (power or amplitude) .and duration.
  • the prosodic parameter values for a word is dependent upon word position in a phrase and locations of natural speech pauses.
  • TTS Text-To-Speech
  • a method for automatically identifying natural speech pauses in a text string comprising: obtaining the text string comprising two ends these ends being a start end and a finish end; .analyzing at least one word in the text string to determine if there is a natural speech pause adjacent to the word, the analyzing being based on at least one predefined threshold value for the word, the threshold value being associated with a number of syllables between the word and one of the two ends of the text string; and inserting the natural speech pause into a synthesized speech signal output representative of the text string.
  • the at least one predefined threshold value includes a P_word threshold value based on the number of syllables between the start end and the word.
  • the at least one predefined threshold value includes a F_word threshold value based on the number of syllables between the finish end and the word.
  • the least one predefined threshold value is determined by the steps of: providing a training set of transcriptions, with at least one natural speech pause identified by an inserted identifier; identifying words in each of the transcriptions as P_words and F_words; statistically analyzing the P_words and F_words in the training set; determining the F_word threshold value and P_word threshold value from results of the Statistically analyzing.
  • the inserting the natural speech pause may also include pauses identified as Part Of Speech (POS) pattern natural breaks.
  • the inserting the natural speech pause may also include pauses identified as Compound word natural pauses.
  • Fig. 1 is a schematic block diagram of an electronic device in accordance with the present invention
  • Fig. 2 illustrates a method 200 for determining threshold values associated with natural speech pauses in text strings
  • Figs 3A to 3D illustrate examples of tanscriptions used for the method of Fig. 2.
  • Fig. 4. illustrates a method for automatically identifying natural speech pauses in a text string
  • Fig 5 is illustrates detailed of an analyzing step of FIG. 4.
  • FIG. 1 there is illustrated an electronic device 100, in the form of a radio-telephone, comprising a device processor 102 operatively coupled by a bus 103 to a user interface 104 that is typically a touch screen or alternatively a display screen and keypad.
  • the electronic device 100 also has an utterance corpus 106, a speech synthesizer 110, Non Volatile memory 120, Read Only Memory 118 and Radio communications module 116 all operatively coupled to the processor 102 by the bus 103.
  • the speech synthesizer 110 has an output coupled to drive a speaker 112.
  • the corpus 106 includes representations of words or phonemes and associated sampled, digitized and processed utterance waveforms PUWs.
  • the Non Volatile memory 120 memory module
  • the waveform utterance corpus comprises transcriptions, representing phrases and corresponding sampled and digitized utter.ance waveforms, text strings at locations relative to the natural phrase boundaries as described below.
  • the radio frequency communications unit 116 is typically a combined receiver and transmitter having a common antenna.
  • the radio frequency communications unit 116 has a transceiver coupled to antenna via a radio frequency amplifier.
  • the transceiver is also coupled to a combined modulator/demodulator that couples the communications unit 116 to the processor 102.
  • the non-volatile memory 120 (memory module) stores a user programmable phonebook database Db and Read Only Memory 118 stores operating code (OC) for device processor 102
  • Fig. 2 there is illustrated a method 200 for determining threshold values associated with natural speech pauses in text strings. The threshold values being based on a number of preceding and following syllables in transcriptions in a training set TS.
  • the method 200 effects a providing step 220 for providing the training set TS of transcriptions, typically sentences, with at least one natural speech pause identified by a manually inserted punctuation mark or identifier "
  • One of these transcriptions 300 is "Based on our history
  • each word in the transcription can be designated as : (i) A P_word that is identified as a word in the transcription with an immediately preceding natural pause identified by the punctuation mark "
  • an identifying step 230 provides for identifying words in each of the transcriptions as (i)a P_word; (ii) a F_word; or (iii)a neutral word.
  • table 1 below identifies attributes of each word in the transcription.
  • the method 200 then performs a statistically analyzing step 240.
  • a heuristic ratio HR of 0.75 is used to determine a P_word break threshold value PT for the word "in".
  • a similar statistical analysis is used to determine at step 250 an F_word break threshold value FT for "in”, again the heuristic ratio HR of 0.75 is used.
  • PT and FT values are determined for all other P_word and F_word instances for all other words in the training set TS (using the heuristic ratio HR of 0.75).
  • the method 200 then ends at step 260 and all P word and F_word instances for all words in the training set TS are stored in the Non- Volatile
  • FIG. 4 there is illustrated a method 400 for automatically identifying natural speech pauses in a text string STR, the pauses being for use in text to speech conversion performed on the electronic device 100.
  • the method 400 effects a step 420 of obtaining the text string STR comprising two ends these ends being a start end SE and a finish end FE.
  • a selecting word- step 430 selects one of the words (or a compound word CW)
  • an analyzing step 440 provides for analyzing at least one word (or compound words CW) in the text string STR to determine if there is a natural speech pause adjacent to the word (or compound words CW), the analyzing being based on at least one predefined threshold value (PT or FT) for the word, the threshold value being associated with a number of syllables between the word and one of the two ends of the text string.
  • the threshold value includes the P_word threshold value PT based on the number of syllables between the start end .and the word.
  • the threshold value includes the F_word threshold value FT based on the number of syllables between the finish end and the word.
  • step 450 determines that a pause was identified by step 440, then a natural speech pause is inserted, at step 460, for speech synthesis. Otherwise no pause is inserted for the word that was selected at the step 430.
  • step 470 checks to determine if all words in the text string STR have been analyzed and if more there words not analyzed the method returns to step
  • a speech synthesis step 480 provides for synthesizing speech at synthesizer 110, using the corpus 106, wherein inserting occurs of the natural speech pause or pauses (inserted into text string STR at step 460) ⁇ into a synthesized speech signal output representative of the text string STR.
  • Fig. 5 there is illustrated a more detailed diagram of the analyzing step 440. Firstly, the text string STR is checked, at test step 441, to determine if it has a Part Of Speech (POS) pattern natural pause break.
  • POS Part Of Speech
  • POS pattern natural break pauses are as follows: 1. numeral + noun For instance: two thousand books. 2. verb + adverb For instance: look carefully
  • a step 446 is effected and the break is identified as a F_word break. If no break is determined at step 441 then text string STR is checked, at test step 442, to determine if it has a Compound word natural pause insertion break.
  • Examples of Compound word natural break pauses are as follows: a bit of a body of a few a fleet of a flooding of a fraction of a function of a good deal a good deal of a great deal a great deal of a growing number of a hint of a large body of a large number of a lot of land a majority of If a break is determined at step 442 then a step 446 is effected and the break is identified as a F_word break. If no break is identified at step 442 then at a step 443 a test determines if the P_word threshold value PT for the selected word has been reached. This is determined by comparing the number of syllables in the text string STR between the start end and the selected word.
  • a natural break is determined and identified as a P_word break at step 444.
  • a test determines if the N_word threshold value PT for the selected word has been reached. This is determined by comparing the number of syllables in the text string STR between the finish end and the selected word. If the F_word threshold value PT is reached then a natural break is determined .and identified as a F_word break at step 446. Otherwise, no break is identified at step 447.
  • the present invention allows for identifying natural speech pauses in text strings for use in Text-To-Speech (TTS) synthesis thereby improving the quality of synthesized speech.
  • TTS Text-To-Speech
  • the detailed description provides a preferred exemplary embodiment only, and is not intended to limit the scope, applicability, or configuration of the invention. Rather, the detailed description of the prefe ⁇ ed exemplary embodiment provides those skilled in the art with an enabling description for implementing preferred exemplary embodiment of the invention. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth in the appended claims.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Machine Translation (AREA)
  • Electrically Operated Instructional Devices (AREA)

Abstract

There is described a method (400) for automatically identifying natural speech pauses in a text string, the pauses being for use in text to speech conversion performed on an electronic device (100). The method (400) includes obtaining (420) the text string comprising two ends, these ends being a start end and a finish end. Then there is effected a step of analyzing (440) at least one word in the text string to determine if there is a natural speech pause adjacent to the word, the analyzing being based on at least one predefined threshold value for the word, the threshold value being associated with a number of syllables between the word and one of the two ends of the text string. Then there is provided a step of inserting (460) the natural speech pause into a synthesized speech signal output representative of the text string.

Description

IDENTIFYING NATURAL SPEECH PAUSES IN A TEXT STRING
FIELD OF THE INVENTION The present invention relates generally to Text-To-Speech (TTS) synthesis. The invention is particularly useful for determining natural pauses in synthesized pronunciation of a text segment.
BACKGROUND OF THE INVENTION Text to Speech (TTS) conversion, often referred to as concatenated text to speech synthesis, allows electronic devices to receive an input text string .and provide a converted representation of the string in the form of synthesized speech. However, a device that may be required to synthesize speech originating from a non-deterministic number of received text strings will have difficulty in providing high quality realistic synthesized speech. That is because the pronunciation of each word or syllable (for Chinese characters and the like) to be synthesized is context and location dependent. For example, a pronunciation of a word at the end of a sentence (input text string) may be drawn out or lengthened. The pronunciation of the same word may be lengthened even more if it occurs in the middle of a sentence where emphasis is required at a natural speech pause. In most languages the pronunciation of a word depends on acoustic prosodic parameters comprising tone (pitch), volume (power or amplitude) .and duration. The prosodic parameter values for a word is dependent upon word position in a phrase and locations of natural speech pauses. However, identification of natural speech pauses for varying random input text patterns does not readily occur in state of the art Text-To-Speech (TTS) synthesis. In this specification, including the claims, the terms 'comprises', 'comprising' or similar terms are intended to mean a non-exclusive inclusion, such that a method or apparatus that comprises a list of elements does not include those elements solely, but may well include other elements not listed. SUMMARY OF THE INVENTION According to one aspect of the invention there is provided a method for automatically identifying natural speech pauses in a text string, the pauses being for use in text to speech conversion performed on an electronic device, the method comprising: obtaining the text string comprising two ends these ends being a start end and a finish end; .analyzing at least one word in the text string to determine if there is a natural speech pause adjacent to the word, the analyzing being based on at least one predefined threshold value for the word, the threshold value being associated with a number of syllables between the word and one of the two ends of the text string; and inserting the natural speech pause into a synthesized speech signal output representative of the text string. Suitably, the at least one predefined threshold value includes a P_word threshold value based on the number of syllables between the start end and the word. Suitably, the at least one predefined threshold value includes a F_word threshold value based on the number of syllables between the finish end and the word. Preferably, the least one predefined threshold value is determined by the steps of: providing a training set of transcriptions, with at least one natural speech pause identified by an inserted identifier; identifying words in each of the transcriptions as P_words and F_words; statistically analyzing the P_words and F_words in the training set; determining the F_word threshold value and P_word threshold value from results of the Statistically analyzing. Suitably, the inserting the natural speech pause may also include pauses identified as Part Of Speech (POS) pattern natural breaks. Suitably, the inserting the natural speech pause may also include pauses identified as Compound word natural pauses.
BRIEF DESCRIPTION OF THE DRAWINGS In order that the invention may be readily understood and put into practical effect, reference will now be made to a preferred embodiment as illustrated with reference to the accompanying drawings in which: Fig. 1 is a schematic block diagram of an electronic device in accordance with the present invention; Fig. 2 illustrates a method 200 for determining threshold values associated with natural speech pauses in text strings; Figs 3A to 3D illustrate examples of tanscriptions used for the method of Fig. 2.; Fig. 4. illustrates a method for automatically identifying natural speech pauses in a text string; and Fig 5 is illustrates detailed of an analyzing step of FIG. 4. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION Referring to Fig. 1 there is illustrated an electronic device 100, in the form of a radio-telephone, comprising a device processor 102 operatively coupled by a bus 103 to a user interface 104 that is typically a touch screen or alternatively a display screen and keypad. The electronic device 100 also has an utterance corpus 106, a speech synthesizer 110, Non Volatile memory 120, Read Only Memory 118 and Radio communications module 116 all operatively coupled to the processor 102 by the bus 103. The speech synthesizer 110 has an output coupled to drive a speaker 112. The corpus 106 includes representations of words or phonemes and associated sampled, digitized and processed utterance waveforms PUWs. In other words, and as described below, the Non Volatile memory 120 (memory module) provides text strings in use for Text-To-Speech (TTS) synthesis (the text may be received by module 116 or otherwise). Also the waveform utterance corpus comprises transcriptions, representing phrases and corresponding sampled and digitized utter.ance waveforms, text strings at locations relative to the natural phrase boundaries as described below. As will be apparent to a person skilled in the art, the radio frequency communications unit 116 is typically a combined receiver and transmitter having a common antenna. The radio frequency communications unit 116 has a transceiver coupled to antenna via a radio frequency amplifier. The transceiver is also coupled to a combined modulator/demodulator that couples the communications unit 116 to the processor 102. Also, in this embodiment the non-volatile memory 120 (memory module) stores a user programmable phonebook database Db and Read Only Memory 118 stores operating code (OC) for device processor 102 Referring to Fig. 2 there is illustrated a method 200 for determining threshold values associated with natural speech pauses in text strings. The threshold values being based on a number of preceding and following syllables in transcriptions in a training set TS. After a start step 210 the method 200 effects a providing step 220 for providing the training set TS of transcriptions, typically sentences, with at least one natural speech pause identified by a manually inserted punctuation mark or identifier "|". Examples of such transcription or sentences are shown in FIGs. 3A to 3D. One of these transcriptions 300 is "Based on our history | in China," has a natural speech pause 310 between the words "history" and "China". Also, there is a start end 305 and a finish end 315 for the transcription 300. As will be apparent to a person skilled in the art, all the transcriptions 300 in FIGs. 3 A to 3D have at least one natural speech pause 310 and a start end 305 and a finish end 315. Further analysis of the transcription shows the following:
Based = 2 syllables on = 1 syllable our = 1 syllable history = 3 syllables in = 1 syllable China = 2 syllables
Also, each word in the transcription can be designated as : (i) A P_word that is identified as a word in the transcription with an immediately preceding natural pause identified by the punctuation mark "|"; (ii) an F_word that is identified as a word in the transcription with an immediately following natural pause identified by the punctuation mark "|"; (iii) a neutral word that does not have an adjacent natural speech pause in the tanscription. After the step 220 .an identifying step 230 provides for identifying words in each of the transcriptions as (i)a P_word; (ii) a F_word; or (iii)a neutral word. Hence, for the transcription "Based on our history | in China,", table 1 below identifies attributes of each word in the transcription.
Table 1 Analysis of the transcription "Based on our history | in China,''
The method 200 then performs a statistically analyzing step 240. In this step 249, if the provided training set TS has 90,000 transcriptions (e.g. sentences) and presuming the word "in" occurs 10,000 times training set TS, then for these 10,000 instances of "in" the following statistical analysis could be observed: (i) Number of occurrences (OPW) of "in" as a P_word = 8,000 instances; (ii) Number of occurrences (OFW) of "in" as a F_word =1,000 instances; (iii) Number of occurrences (ONW) of "in" as a neutral word (a word that is neither a P_word or F_word) = 1,000 instances; Further, from the 8,000 P_word occurrences (OPW) of "in" identified in the training set TS, the following statistical analysis could be observed: (i) Occurrences (OPS) of 8 or more preceding syllables = 0; (ii) Occurrences (OPS) of 7 preceding syllables = 400; (iii) Occurrences (OPS) of 6 preceding syllables = 600; (iv) Occurrences (OPS) of 5 preceding syllables = 2,000; (v) Occurrences (OPS) of 4 preceding syllables = 3,000; (vi) Occurrences (OPS) of 3 preceding syllables = 1,000; (vii) Occurrences (OPS) of 2 preceding syllables = 1,000; (viii) Occurrences (OPS) of 1 preceding syllables = 0.
A heuristic ratio HR of 0.75, selected by intuition and experimentation, is used to determine a P_word break threshold value PT for the word "in". This threshold value PT is determined at a determining threshold values step 250 as follows: starting from the maximum number of observed syllables to the minimum number of observed syllables Do from largest OPS Until: Sum OPS/ OPW >= 0.75 Select PT to be the number of observed syllables identified by the last OPS of Sum OPS; End Do.
Thus PT for "in" would be determined at step 250 as follows: 400/8,000 = 0.05 for 7 preceding syllables; (400+600)78,000 = 0.125 for 6 preceding syllables; (400+600+2,000)/8,000 = 0.375 for 5 preceding syllables; (400+600+2,000+35000)/8,000 = 0.75 for 4 preceding syllables and thus PT is selected to be 4. A similar statistical analysis is used to determine at step 250 an F_word break threshold value FT for "in", again the heuristic ratio HR of 0.75 is used. Also, PT and FT values are determined for all other P_word and F_word instances for all other words in the training set TS (using the heuristic ratio HR of 0.75). The method 200 then ends at step 260 and all P word and F_word instances for all words in the training set TS are stored in the Non- Volatile
Memory 120. Referring to Fig. 4, there is illustrated a method 400 for automatically identifying natural speech pauses in a text string STR, the pauses being for use in text to speech conversion performed on the electronic device 100. After a start step 410, the method 400 effects a step 420 of obtaining the text string STR comprising two ends these ends being a start end SE and a finish end FE. A selecting word- step 430 selects one of the words (or a compound word CW)
.and an analyzing step 440 provides for analyzing at least one word (or compound words CW) in the text string STR to determine if there is a natural speech pause adjacent to the word (or compound words CW), the analyzing being based on at least one predefined threshold value (PT or FT) for the word, the threshold value being associated with a number of syllables between the word and one of the two ends of the text string. The threshold value includes the P_word threshold value PT based on the number of syllables between the start end .and the word. Also, the threshold value includes the F_word threshold value FT based on the number of syllables between the finish end and the word. If a test step 450 determines that a pause was identified by step 440, then a natural speech pause is inserted, at step 460, for speech synthesis. Otherwise no pause is inserted for the word that was selected at the step 430. As step 470 then checks to determine if all words in the text string STR have been analyzed and if more there words not analyzed the method returns to step
230. Otherwise, a speech synthesis step 480 provides for synthesizing speech at synthesizer 110, using the corpus 106, wherein inserting occurs of the natural speech pause or pauses (inserted into text string STR at step 460) ι into a synthesized speech signal output representative of the text string STR. Referring to Fig. 5, there is illustrated a more detailed diagram of the analyzing step 440. Firstly, the text string STR is checked, at test step 441, to determine if it has a Part Of Speech (POS) pattern natural pause break.
Examples of POS pattern natural break pauses are as follows: 1. numeral + noun For instance: two thousand books. 2. verb + adverb For instance: look carefully
3. preposition + noun For instance: with telescopes
4. adjective + noun For instance: beautiful city
If a break is determined at step 441 then a step 446 is effected and the break is identified as a F_word break. If no break is determined at step 441 then text string STR is checked, at test step 442, to determine if it has a Compound word natural pause insertion break. Examples of Compound word natural break pauses are as follows: a bit of a body of a few a fleet of a flooding of a fraction of a function of a good deal a good deal of a great deal a great deal of a growing number of a hint of a large body of a large number of a lot of land a majority of If a break is determined at step 442 then a step 446 is effected and the break is identified as a F_word break. If no break is identified at step 442 then at a step 443 a test determines if the P_word threshold value PT for the selected word has been reached. This is determined by comparing the number of syllables in the text string STR between the start end and the selected word. If the P_word threshold value PT is reached then a natural break is determined and identified as a P_word break at step 444. Alternatively, if no break is identified at step 443 then at a step 445 a test determines if the N_word threshold value PT for the selected word has been reached. This is determined by comparing the number of syllables in the text string STR between the finish end and the selected word. If the F_word threshold value PT is reached then a natural break is determined .and identified as a F_word break at step 446. Otherwise, no break is identified at step 447. Advantageously, the present invention allows for identifying natural speech pauses in text strings for use in Text-To-Speech (TTS) synthesis thereby improving the quality of synthesized speech. The detailed description provides a preferred exemplary embodiment only, and is not intended to limit the scope, applicability, or configuration of the invention. Rather, the detailed description of the prefeπed exemplary embodiment provides those skilled in the art with an enabling description for implementing preferred exemplary embodiment of the invention. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth in the appended claims.

Claims

WE CLAIM:
1. A method for automatically identifying natural speech pauses in a text string, the pauses being for use in text to speech conversion performed on an electronic device, the method comprising: obtaining the text string comprising two ends these ends being a start end and a finish end; analyzing at least one word in the text string to determine if there is a natural speech pause adjacent to the word, the analyzing being based on at least one predefined threshold value for the word, the threshold value being associated with a number of syllables between the word and one of the two ends of the text string; .and inserting the natural speech pause into a synthesized speech signal output representative of the text string.
2. A method for automatically identifying natural speech pauses in a text string, as claimed in claim 1, wherein the at least one predefined threshold value includes a P_word threshold value based on the number of syllables between the start end and the word.
3. A method for automatically identifying natural speech pauses in a text string, as claimed in claim 1, wherein the at least one predefined threshold value includes a F_word threshold value based on the number of syllables between the finish end .and the word.
4. A method for automatically identifying natural speech pauses in a text string, as claimed in claim 1, wherein the least one predefined threshold value is determined by the steps of: providing a training set of transcriptions, with at least one natural speech pause identified by an inserted identifier; identifying words in each of the transcriptions as P_words and F_words; statistically analyzing the P_words and F_words in the training set; determining the F_word threshold value and P_word threshold value from results of the Statistically analyzing.
5. A method for automatically identifying natural speech pauses in a text string, as claimed in claim 1, wherein the inserting the natural speech pause may also include pauses identified as Part Of Speech (POS) pattern natural breaks.
6. A method for automatically identifying natural speech pauses in a text string, as claimed in claim 1, wherein, the inserting the natural speech pause may also include pauses identified as Compound word natural pauses.
EP04784433A 2003-09-29 2004-09-17 Identifying natural speech pauses in a text string Withdrawn EP1668631A4 (en)

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CNB031327087A CN1320482C (en) 2003-09-29 2003-09-29 Natural voice pause in identification text strings
PCT/US2004/030570 WO2005034085A1 (en) 2003-09-29 2004-09-17 Identifying natural speech pauses in a text string

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Families Citing this family (124)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8645137B2 (en) 2000-03-16 2014-02-04 Apple Inc. Fast, language-independent method for user authentication by voice
CN1260704C (en) * 2003-09-29 2006-06-21 摩托罗拉公司 Method for voice synthesizing
US8677377B2 (en) 2005-09-08 2014-03-18 Apple Inc. Method and apparatus for building an intelligent automated assistant
US9318108B2 (en) 2010-01-18 2016-04-19 Apple Inc. Intelligent automated assistant
WO2008075076A2 (en) * 2006-12-21 2008-06-26 Symbian Software Limited Communicating information
US8977255B2 (en) 2007-04-03 2015-03-10 Apple Inc. Method and system for operating a multi-function portable electronic device using voice-activation
US9330720B2 (en) 2008-01-03 2016-05-03 Apple Inc. Methods and apparatus for altering audio output signals
US8996376B2 (en) 2008-04-05 2015-03-31 Apple Inc. Intelligent text-to-speech conversion
US10496753B2 (en) 2010-01-18 2019-12-03 Apple Inc. Automatically adapting user interfaces for hands-free interaction
US20100030549A1 (en) 2008-07-31 2010-02-04 Lee Michael M Mobile device having human language translation capability with positional feedback
US8396714B2 (en) 2008-09-29 2013-03-12 Apple Inc. Systems and methods for concatenation of words in text to speech synthesis
US8352272B2 (en) 2008-09-29 2013-01-08 Apple Inc. Systems and methods for text to speech synthesis
US8712776B2 (en) 2008-09-29 2014-04-29 Apple Inc. Systems and methods for selective text to speech synthesis
US8352268B2 (en) 2008-09-29 2013-01-08 Apple Inc. Systems and methods for selective rate of speech and speech preferences for text to speech synthesis
US9959870B2 (en) 2008-12-11 2018-05-01 Apple Inc. Speech recognition involving a mobile device
US8380507B2 (en) 2009-03-09 2013-02-19 Apple Inc. Systems and methods for determining the language to use for speech generated by a text to speech engine
US10241752B2 (en) 2011-09-30 2019-03-26 Apple Inc. Interface for a virtual digital assistant
US9858925B2 (en) 2009-06-05 2018-01-02 Apple Inc. Using context information to facilitate processing of commands in a virtual assistant
US10255566B2 (en) 2011-06-03 2019-04-09 Apple Inc. Generating and processing task items that represent tasks to perform
US10241644B2 (en) 2011-06-03 2019-03-26 Apple Inc. Actionable reminder entries
US9431006B2 (en) 2009-07-02 2016-08-30 Apple Inc. Methods and apparatuses for automatic speech recognition
US10553209B2 (en) 2010-01-18 2020-02-04 Apple Inc. Systems and methods for hands-free notification summaries
US10705794B2 (en) 2010-01-18 2020-07-07 Apple Inc. Automatically adapting user interfaces for hands-free interaction
US10276170B2 (en) 2010-01-18 2019-04-30 Apple Inc. Intelligent automated assistant
US10679605B2 (en) 2010-01-18 2020-06-09 Apple Inc. Hands-free list-reading by intelligent automated assistant
DE202011111062U1 (en) 2010-01-25 2019-02-19 Newvaluexchange Ltd. Device and system for a digital conversation management platform
US8682667B2 (en) 2010-02-25 2014-03-25 Apple Inc. User profiling for selecting user specific voice input processing information
US10762293B2 (en) 2010-12-22 2020-09-01 Apple Inc. Using parts-of-speech tagging and named entity recognition for spelling correction
US9262612B2 (en) 2011-03-21 2016-02-16 Apple Inc. Device access using voice authentication
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
US8994660B2 (en) 2011-08-29 2015-03-31 Apple Inc. Text correction processing
US10134385B2 (en) 2012-03-02 2018-11-20 Apple Inc. Systems and methods for name pronunciation
US9483461B2 (en) 2012-03-06 2016-11-01 Apple Inc. Handling speech synthesis of content for multiple languages
US9280610B2 (en) 2012-05-14 2016-03-08 Apple Inc. Crowd sourcing information to fulfill user requests
US9721563B2 (en) 2012-06-08 2017-08-01 Apple Inc. Name recognition system
US9495129B2 (en) 2012-06-29 2016-11-15 Apple Inc. Device, method, and user interface for voice-activated navigation and browsing of a document
US9576574B2 (en) 2012-09-10 2017-02-21 Apple Inc. Context-sensitive handling of interruptions by intelligent digital assistant
US9547647B2 (en) 2012-09-19 2017-01-17 Apple Inc. Voice-based media searching
DE112014000709B4 (en) 2013-02-07 2021-12-30 Apple Inc. METHOD AND DEVICE FOR OPERATING A VOICE TRIGGER FOR A DIGITAL ASSISTANT
US9368114B2 (en) 2013-03-14 2016-06-14 Apple Inc. Context-sensitive handling of interruptions
WO2014144579A1 (en) 2013-03-15 2014-09-18 Apple Inc. System and method for updating an adaptive speech recognition model
WO2014144949A2 (en) 2013-03-15 2014-09-18 Apple Inc. Training an at least partial voice command system
WO2014197334A2 (en) 2013-06-07 2014-12-11 Apple Inc. System and method for user-specified pronunciation of words for speech synthesis and recognition
US9582608B2 (en) 2013-06-07 2017-02-28 Apple Inc. Unified ranking with entropy-weighted information for phrase-based semantic auto-completion
WO2014197336A1 (en) 2013-06-07 2014-12-11 Apple Inc. System and method for detecting errors in interactions with a voice-based digital assistant
WO2014197335A1 (en) 2013-06-08 2014-12-11 Apple Inc. Interpreting and acting upon commands that involve sharing information with remote devices
US10176167B2 (en) 2013-06-09 2019-01-08 Apple Inc. System and method for inferring user intent from speech inputs
KR101959188B1 (en) 2013-06-09 2019-07-02 애플 인크. Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant
KR101809808B1 (en) 2013-06-13 2017-12-15 애플 인크. System and method for emergency calls initiated by voice command
KR101749009B1 (en) 2013-08-06 2017-06-19 애플 인크. Auto-activating smart responses based on activities from remote devices
US9620105B2 (en) 2014-05-15 2017-04-11 Apple Inc. Analyzing audio input for efficient speech and music recognition
US10592095B2 (en) 2014-05-23 2020-03-17 Apple Inc. Instantaneous speaking of content on touch devices
US9502031B2 (en) 2014-05-27 2016-11-22 Apple Inc. Method for supporting dynamic grammars in WFST-based ASR
US9734193B2 (en) 2014-05-30 2017-08-15 Apple Inc. Determining domain salience ranking from ambiguous words in natural speech
US9760559B2 (en) 2014-05-30 2017-09-12 Apple Inc. Predictive text input
US9430463B2 (en) 2014-05-30 2016-08-30 Apple Inc. Exemplar-based natural language processing
WO2015184186A1 (en) 2014-05-30 2015-12-03 Apple Inc. Multi-command single utterance input method
US10289433B2 (en) 2014-05-30 2019-05-14 Apple Inc. Domain specific language for encoding assistant dialog
US9633004B2 (en) 2014-05-30 2017-04-25 Apple Inc. Better resolution when referencing to concepts
US10170123B2 (en) 2014-05-30 2019-01-01 Apple Inc. Intelligent assistant for home automation
US9785630B2 (en) 2014-05-30 2017-10-10 Apple Inc. Text prediction using combined word N-gram and unigram language models
US9715875B2 (en) 2014-05-30 2017-07-25 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US10078631B2 (en) 2014-05-30 2018-09-18 Apple Inc. Entropy-guided text prediction using combined word and character n-gram language models
US9842101B2 (en) 2014-05-30 2017-12-12 Apple Inc. Predictive conversion of language input
US9338493B2 (en) 2014-06-30 2016-05-10 Apple Inc. Intelligent automated assistant for TV user interactions
US10659851B2 (en) 2014-06-30 2020-05-19 Apple Inc. Real-time digital assistant knowledge updates
US10446141B2 (en) 2014-08-28 2019-10-15 Apple Inc. Automatic speech recognition based on user feedback
US9818400B2 (en) 2014-09-11 2017-11-14 Apple Inc. Method and apparatus for discovering trending terms in speech requests
US9542929B2 (en) 2014-09-26 2017-01-10 Intel Corporation Systems and methods for providing non-lexical cues in synthesized speech
US9886432B2 (en) 2014-09-30 2018-02-06 Apple Inc. Parsimonious handling of word inflection via categorical stem + suffix N-gram language models
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
US10127911B2 (en) 2014-09-30 2018-11-13 Apple Inc. Speaker identification and unsupervised speaker adaptation techniques
US10074360B2 (en) 2014-09-30 2018-09-11 Apple Inc. Providing an indication of the suitability of speech recognition
US9646609B2 (en) 2014-09-30 2017-05-09 Apple Inc. Caching apparatus for serving phonetic pronunciations
US10552013B2 (en) 2014-12-02 2020-02-04 Apple Inc. Data detection
US9711141B2 (en) 2014-12-09 2017-07-18 Apple Inc. Disambiguating heteronyms in speech synthesis
US9865280B2 (en) 2015-03-06 2018-01-09 Apple Inc. Structured dictation using intelligent automated assistants
US9886953B2 (en) 2015-03-08 2018-02-06 Apple Inc. Virtual assistant activation
US9721566B2 (en) 2015-03-08 2017-08-01 Apple Inc. Competing devices responding to voice triggers
US10567477B2 (en) 2015-03-08 2020-02-18 Apple Inc. Virtual assistant continuity
US9899019B2 (en) 2015-03-18 2018-02-20 Apple Inc. Systems and methods for structured stem and suffix language models
US9842105B2 (en) 2015-04-16 2017-12-12 Apple Inc. Parsimonious continuous-space phrase representations for natural language processing
US10083688B2 (en) 2015-05-27 2018-09-25 Apple Inc. Device voice control for selecting a displayed affordance
US10127220B2 (en) 2015-06-04 2018-11-13 Apple Inc. Language identification from short strings
US10101822B2 (en) 2015-06-05 2018-10-16 Apple Inc. Language input correction
US9578173B2 (en) 2015-06-05 2017-02-21 Apple Inc. Virtual assistant aided communication with 3rd party service in a communication session
US10255907B2 (en) 2015-06-07 2019-04-09 Apple Inc. Automatic accent detection using acoustic models
US11025565B2 (en) 2015-06-07 2021-06-01 Apple Inc. Personalized prediction of responses for instant messaging
US10186254B2 (en) 2015-06-07 2019-01-22 Apple Inc. Context-based endpoint detection
US10747498B2 (en) 2015-09-08 2020-08-18 Apple Inc. Zero latency digital assistant
US10671428B2 (en) 2015-09-08 2020-06-02 Apple Inc. Distributed personal assistant
US9697820B2 (en) 2015-09-24 2017-07-04 Apple Inc. Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks
US11010550B2 (en) 2015-09-29 2021-05-18 Apple Inc. Unified language modeling framework for word prediction, auto-completion and auto-correction
US10366158B2 (en) 2015-09-29 2019-07-30 Apple Inc. Efficient word encoding for recurrent neural network language models
US11587559B2 (en) 2015-09-30 2023-02-21 Apple Inc. Intelligent device identification
US10691473B2 (en) 2015-11-06 2020-06-23 Apple Inc. Intelligent automated assistant in a messaging environment
US10049668B2 (en) 2015-12-02 2018-08-14 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
US10223066B2 (en) 2015-12-23 2019-03-05 Apple Inc. Proactive assistance based on dialog communication between devices
US10446143B2 (en) 2016-03-14 2019-10-15 Apple Inc. Identification of voice inputs providing credentials
US9934775B2 (en) 2016-05-26 2018-04-03 Apple Inc. Unit-selection text-to-speech synthesis based on predicted concatenation parameters
US9972304B2 (en) 2016-06-03 2018-05-15 Apple Inc. Privacy preserving distributed evaluation framework for embedded personalized systems
US10249300B2 (en) 2016-06-06 2019-04-02 Apple Inc. Intelligent list reading
US10049663B2 (en) 2016-06-08 2018-08-14 Apple, Inc. Intelligent automated assistant for media exploration
DK179588B1 (en) 2016-06-09 2019-02-22 Apple Inc. Intelligent automated assistant in a home environment
US10509862B2 (en) 2016-06-10 2019-12-17 Apple Inc. Dynamic phrase expansion of language input
US10067938B2 (en) 2016-06-10 2018-09-04 Apple Inc. Multilingual word prediction
US10192552B2 (en) 2016-06-10 2019-01-29 Apple Inc. Digital assistant providing whispered speech
US10490187B2 (en) 2016-06-10 2019-11-26 Apple Inc. Digital assistant providing automated status report
US10586535B2 (en) 2016-06-10 2020-03-10 Apple Inc. Intelligent digital assistant in a multi-tasking environment
DK179049B1 (en) 2016-06-11 2017-09-18 Apple Inc Data driven natural language event detection and classification
DK201670540A1 (en) 2016-06-11 2018-01-08 Apple Inc Application integration with a digital assistant
DK179343B1 (en) 2016-06-11 2018-05-14 Apple Inc Intelligent task discovery
DK179415B1 (en) 2016-06-11 2018-06-14 Apple Inc Intelligent device arbitration and control
US10043516B2 (en) 2016-09-23 2018-08-07 Apple Inc. Intelligent automated assistant
US10593346B2 (en) 2016-12-22 2020-03-17 Apple Inc. Rank-reduced token representation for automatic speech recognition
DK201770439A1 (en) 2017-05-11 2018-12-13 Apple Inc. Offline personal assistant
DK179496B1 (en) 2017-05-12 2019-01-15 Apple Inc. USER-SPECIFIC Acoustic Models
DK179745B1 (en) 2017-05-12 2019-05-01 Apple Inc. SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT
DK201770431A1 (en) 2017-05-15 2018-12-20 Apple Inc. Optimizing dialogue policy decisions for digital assistants using implicit feedback
DK201770432A1 (en) 2017-05-15 2018-12-21 Apple Inc. Hierarchical belief states for digital assistants
DK179560B1 (en) 2017-05-16 2019-02-18 Apple Inc. Far-field extension for digital assistant services
CN110970013A (en) * 2019-12-23 2020-04-07 出门问问信息科技有限公司 Speech synthesis method, device and computer readable storage medium
CN111667816B (en) * 2020-06-15 2024-01-23 北京百度网讯科技有限公司 Model training method, speech synthesis method, device, equipment and storage medium
CN114664283A (en) * 2022-02-28 2022-06-24 阿里巴巴（中国）有限公司 Text processing method in speech synthesis and electronic equipment

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SU485492A1 (en) * 1973-01-09 1975-09-25 Минский радиотехнический институт Speech synthesis device
JPH05160773A (en) * 1991-12-03 1993-06-25 Toshiba Corp Voice communication equipment
JP3060422B2 (en) * 1992-08-11 2000-07-10 株式会社エイ・ティ・アール自動翻訳電話研究所 Voice rule synthesizer
US5692104A (en) * 1992-12-31 1997-11-25 Apple Computer, Inc. Method and apparatus for detecting end points of speech activity
DE69425564D1 (en) * 1993-03-12 2000-09-21 Stanford Res Inst Int METHOD AND DEVICE FOR VOICE TEACHING BY MEANS OF INTERACTIVE VOICE CONTROL
EP0680653B1 (en) * 1993-10-15 2001-06-20 AT&T Corp. A method for training a tts system, the resulting apparatus, and method of use thereof
CN1032391C (en) * 1994-04-01 1996-07-24 清华大学 Chinese character-phonetics transfer method and system edited based on waveform
CN1139038C (en) * 2000-06-22 2004-02-18 上海贝尔有限公司 By-pass method for dialing access service of internet
JP3690502B2 (en) * 2001-04-19 2005-08-31 日本電信電話株式会社 Prosodic information setting method and apparatus, program and recording medium
JP4635384B2 (en) * 2001-07-03 2011-02-23 日本電気株式会社 Speech synthesis system, speech synthesis method, and speech synthesis program

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