KR102080409B1 - Lecture voice file text conversion system based on lecture-related keywords - Google Patents

Abstract

The present invention relates to a lecture voice file text conversion system based on lecture related keywords. More specifically, by providing the keyword input module of the lecture, the weight of each keyword can be applied, and the search module applies the weight to the search module. Can be converted into text, so that words do not deviate from the subject of the lecture as much as possible, improve speech recognition accuracy, and have a correction module, so that the syllables, neutral, and The lecture voice based on the lecture-related keywords that the finality is differentially marked according to the degree of coincidence with the detected words in the related word detection module, and confirmed by the user correction module, and the text can be corrected, thereby reducing the user's working time. File text conversion system.
To this end, the present invention is a lecture recording module for recording the voice of the lecture; A text conversion module for converting the voice recorded by the lecture recording module into text by performing voice recognition; A lecture keyword input module for inputting keywords related to a specific subject of a lecture recorded by the lecture recording module; A search module for searching by a keyword inputted into a lecture keyword input module in a web server and sorting the search results in order of the number of times a keyword is included in a web page; A text collecting module configured to extract and store text posted on web pages up to a predetermined rank among web pages searched by the search module; An associated word detection module for detecting a word repeated two or more times among the text collected by the text collecting module; An error detection module detecting a word having no information among the texts converted into the text conversion module based on the part-of-speech information and the pronunciation information of each word in connection with a dictionary providing service; A correction module for comparing the word detected by the error detection module with the word detected by the related word detection module and correcting the detected word in the related word detection module if it meets a specific condition, and marking it differently according to the specific condition; And it provides a user correction module that can confirm the modified portion with a different marker, and correct the text by the correction module.

Description

Lecture voice file text conversion system based on lecture-related keywords}

The present invention relates to a lecture voice file text conversion system based on lecture related keywords. More specifically, by providing the keyword input module of the lecture, the weight of each keyword can be applied, and the search module applies the weight to the search module, so that the voice of the lecture is based on the text of the webpage composed of terms frequently used during the lecture. Can be converted into text, so that words do not deviate from the subject of the lecture as much as possible, improve speech recognition accuracy, and have a correction module, so that the syllables, neutral, and The lecture voice based on the lecture-related keywords that the finality is differentially marked according to the degree of coincidence with the detected word in the related word detection module, and confirmed by the user correction module, and the text can be corrected, thereby reducing the user's working time. File text conversion system.

Recently, voice recognition technologies such as Apple's Siri, Google Voice, and Samsung's S-Voice have become popular, and wearable devices such as smart watches and Google Glass are expected to use voice recognition as the most important means of inputting text. Although the technology has been developed for a long time, a large number of errors still occur in inputting texts through accurate speech recognition.

When inputting by voice recognition, the text is replaced with text and is displayed to the user, and the recognized text is frequently different from the intention or there is an error.

In particular, the API, which is a tool that converts voice to text, provides a system that can convert voice to text, but when voice is converted to text, there are differences in how people speak, voice, and noise and noise. There is a problem that the accuracy is low due to the factor.

In addition, in the case of lectures, when the lecture voice content is converted to text and used as a subtitle, the accuracy of the lecture text file is required, but it does not meet expectations including recognition errors.

In addition, the file recording the lecture has a large capacity, there is a limit to keep, there is a problem to search for a specific phrase in the future, there is a problem that there is a restriction in the secondary data utilization through data analysis later.

Prior Art Document: KR Patent No. 1165909 (announced Aug. 29, 2016)

The present invention has been made to solve the above problems, and an object of the present invention is to provide a lecture voice file text conversion system based on lecture-related keywords with high text extraction accuracy from lecture voice files.

Lecture voice file text conversion system based on the lecture-related keywords according to the present invention devised to achieve the above object comprises a lecture recording module for recording the voice of the lecture; A text conversion module for converting the voice recorded by the lecture recording module into text by performing voice recognition; A lecture keyword input module for inputting keywords related to a specific subject of a lecture recorded by the lecture recording module; A search module for searching by a keyword inputted into a lecture keyword input module in a web server and sorting the search results in order of the number of times a keyword is included in a web page; A text collecting module configured to extract and store text posted on web pages up to a predetermined rank among web pages searched by the search module; An associated word detection module for detecting a word repeated two or more times among the text collected by the text collecting module; An error detection module connected to a dictionary providing service and detecting a word having no information among texts converted into a text conversion module based on parts of speech information and pronunciation information of each word; A correction module for comparing the word detected by the error detection module with the word detected by the related word detection module and correcting the detected word in the related word detection module if it meets a specific condition, and marking it differently according to the specific condition; And a user correction module for identifying the modified part with the different markings and correcting the text.

In addition, when the initiality of the words detected in the error detection module matches the initiality of the words detected in the related word detection module, the correction module corrects the detected words in the related word detection module, marks them, and displays the error in the error detection module. If the initiality of the detected word matches the initiality of the detected word in the related word detection module, and if one or more neutrals match, correct the detected word in the related word detection module, mark it differently, and detect it in the error detection module. If the initiality of the detected words matches the initiality of the words detected in the related word detection module, the one or more neutrals match, and the one or more finality matches, the corrected words are corrected to the detected words in the related word detection module and marked differently. It may include doing.

In addition, the lecture keyword input module may assign a weight to each input keyword, and may include calculating a number of keyword inclusions by applying a weight to a search module when searching.

According to the present invention, by providing a keyword input module of the lecture, it is possible to apply the weight for each keyword, and to search by applying the weight with the search module, based on the text of the web page composed of terms frequently used in the lecture The voice of the lecture can be converted into text, which helps to ensure that words do not deviate from the subject of the lecture as much as possible and improves speech recognition accuracy.

In addition, according to the present invention, by providing a correction module, the initial consonant, the neutral, and the finality of each syllable constituting the word detected by the error detection module are differentially marked according to the degree of agreement with the word detected by the related word detection module. The correction module can check and correct this, which has the effect of increasing accuracy and reducing the user's working time.

1 is a conceptual diagram of a lecture voice file text conversion system based on lecture related keywords according to a preferred embodiment of the present invention.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. First of all, in adding reference numerals to the components of each drawing, it should be noted that the same reference numerals have the same reference numerals as much as possible even if displayed on different drawings. In addition, in describing the present invention, if it is determined that the detailed description of the related well-known configuration or function may obscure the gist of the present invention, the detailed description thereof will be omitted. In addition, preferred embodiments of the present invention will be described below, but the technical spirit of the present invention is not limited thereto, but may be variously modified and modified by those skilled in the art.

1 is a conceptual diagram of a lecture voice file text conversion system 1000 based on lecture related keywords according to a preferred embodiment of the present invention.

The lecture voice file text conversion system 1000 based on the lecture related keyword according to the preferred embodiment of the present invention, referring to FIG. 1, the lecture recording module 10, the noise removing module 20, and the text conversion module ( 30, lecture keyword input module 40, search module 50, text collection module 60, related word detection module 70, error detection module 80, correction module 90, and user correction module ( 100), and it is easy to convert the recorded voice to text during the lecture, and improve the accuracy to save the user time when creating the lecture subtitles.

Hereinafter, the lecture recording module 10 will be described in detail.

Lecture recording module 10 is to record the voice of the lecture, if the image is required to include the voice in the video shooting, in the present invention to convert the voice to text to obtain a voice file.

The noise removing module 20 extracts a frequency by reproducing voice data recorded by the lecture recording module 10, and removes an external noise portion except for a frequency of a specific section in which a human voice is irradiated in the frequency domain. In particular, the speech and noise section recognition and removal can be removed in various ways using a spectral subtraction method or an LMS algorithm adaptive filter.

In addition, the noise removing module 20 is a section in which the frequency of the human voice is irradiated while removing the noise portion, and when the voice frequency below a certain duration lasts for a certain time, the part where the lecturer rests (a portion of the text to be written) Can be determined), and the file can be separated and stored.

In the present invention, the separate storage is because the voice recognition rate and the accuracy are higher in converting each part separately from the text to the text rather than converting the long voice file into the text.

The text conversion module 30 converts the voice recorded by the lecture recording module 10 and the noise removal module 20 into speech by performing noise recognition, and converts the text into an API ( Application Programming Interface) to convert the speech to text, and the API can be used for speech text conversion (ex. Google speech API, IBM watson speech) used in the prior art, and is not limited to the type.

The lecture keyword input module 40 inputs a keyword related to a specific subject of a lecture recorded by the lecture recording module 10, and may assign weights to each of the input keywords. Can be applied to calculate the number of keywords included.

In particular, the lecture keyword input module 40 may individually apply keyword weights, and search by applying weights to the search module 50 based on the text of a webpage composed of terms that are frequently used during the lecture. Since the voice of the lecture can be converted into text, the words do not deviate as much as possible from the subject of the lecture during the conversion, and there is an advantage of improving the speech recognition accuracy.

For example, assuming that a lecture on "How to make makgeolli" is given, the keyword input module 40 inputs keywords including makgeolli, carbon dioxide, yeast, yeast, and the like, which are related to the subject of the lecture, and weights each keyword. Grant. When rice wine is weighted as 50%, carbon dioxide 10%, yeast 5%, and yeast 30%, even if the rice wine is listed once on the web page searched through the search module 50, the weight is given three times. In order to calculate the high word weights that are closely related to the subject of the lecture, it is possible to search satisfactorily.

The search module 50 searches for the keyword inputted to the lecture keyword input module 40 in the web server, and sorts the search results in order of the number of times the keyword is included in the web page.

In addition, if a specific keyword is weighted by the lecture keyword input module 40, the search results may be provided in order by applying the weight.

The text collection module 60 extracts the text posted on the web page up to a certain rank among the web pages searched by the search module 50 and stores the text in the DB.

The schedule ranking may be changed according to a user's setting, and the text collecting module 60 may collect text determined to be highly related to the lecture contents through the search module 50.

The related word detection module 70 detects a word repeated two or more times among the text collected by the text collecting module 60. The word repeated two or more times may be regarded as a word that is closely related to the subject of the lecture, and the speech recognition error text is corrected based on the words.

The error detection module 80 detects a word having no information in the text converted by the text conversion module 30 based on the part-of-speech information and the pronunciation information of each word in connection with a dictionary providing service of the web server. The detected word is most likely an error in speech recognition.

The correction module 90 compares the word detected by the error detection module 80 with the word detected by the related word detection module 70 and corrects the word detected by the related word detection module 70 if it meets a specific condition. And it is labeled differently according to specific conditions.

In addition, specific conditions of the modification module 90 will be described in detail.

The correction module 90 corrects the word detected by the related word detection module 70 when the initiality of the word detected by the error detection module 80 matches that of the word detected by the related word detection module 70. And mark it.

In addition, the correction module 90, if the initiality of the words detected by the error detection module 80 matches the initiality of the words detected by the related word detection module 70, and the neutrality matches one or more, the related word detection module Correct the word detected at 70 and mark it differently.

Further, the correction module 90 matches the initiality of the words detected by the error detection module 80 with the initiality of the words detected by the related word detection module 70, one or more neutrals, and one or more finality matches. If so, the word detected by the relevant word detection module 70 is corrected, and it is marked differently.

For example, when the error detection module 80 detects "discrete data," which is a speech recognition error that is not found in the dictionary, a word of the related word detection module 70 (a plurality of words collected by the text collection module 60). Of the repeated words) to see how closely the initial, neutral, and final species match.

The error word "dispersed data" is the word of the related word detection module 70, "carbon dioxide" and the initial "ㅇ", "ㅅ (acid)", "ㅎ (하)", "ㅌ (because)" , Five matches with 'ㅅ (소)', four with neutrality with 'ㅣ (이)', 'ㅏ (mountain)', 'ㅏ (be)', 'ㅗ (소)', In baptism, 'b' coincides with 'b' in 'mountain', and 'b' and 's' in 'tan' disagree. Accordingly, in the present invention, all of the initial castles match, and the neutrality and the finality match one or more, so that blue markings can be performed.

If the error word is "Isahatasso," then "carbon dioxide" and all four stars match all four, and four neutrals match, but the finality is inconsistent. can do.

In addition, if the error word is "subtle", "carbon dioxide" and "super" match all 5, neutrals do not match, and "one" matches "san" in "line", but one matches. Since there is no neutral match, only the first star can be matched, so it can be corrected to "CO2" and marked with purple except blue and green.

In addition, when the initial or final consonants of an error word or a correction word are twin consonants (ㄲ, ㄸ, ㅃ, ㅆ, ㅉ), the consonants and consonants are counted in the same category to determine whether they match.

More specifically, if the error word is "brows", "eyebrows" and the first consonant ('ㄴ') match, but "brows" are often pronounced as "sup" due to old pronunciation, 'Sub' consonant 'ㅅ' is consonant with the consonant 'ㅆ' (the same category), so that the two consonants are judged to match.

In addition, when the initial, neutral, and finality do not match, a separate marking is made so that the user correction module 100 can check and correct it.

For example, when "carbon dioxide" is recognized as "Isanatas", both the initial and the trailing species do not coincide, and the neutrality is three such as 'ㅣ (이)', 'ㅏ (acid)' and 'ㅏ (tan)'. The same does not correspond to the above description. It can be marked with a separate index red except for blue, green and purple.

In addition, the modification module 90 may be used by changing the conditions as appropriate to the user's environment.

The user correction module 100 may identify the portion modified by the modification module 90 with different marks, and may correct the text.

The above description is merely illustrative of the technical idea of the present invention, and various modifications, changes, and substitutions may be made by those skilled in the art without departing from the essential characteristics of the present invention. will be. Accordingly, the embodiments disclosed in the present invention and the accompanying drawings are not intended to limit the technical spirit of the present invention but to describe the present invention, and the scope of the technical idea of the present invention is not limited by the embodiments and the accompanying drawings. . The protection scope of the present invention should be interpreted by the following claims, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present invention.

10-Lecture Recording Module
20-noise reduction module
30-text conversion module
40-Lesson Keyword Input Module
50-search module
60-Text Collection Module
70-Related Word Detection Module
80-error detection module
90-fix module
100-user correction module
1000-Lecture speech file text conversion system based on lecture related keywords

Claims (3)

A lecture recording module for recording a lecture voice;
A text conversion module for converting the voice recorded by the lecture recording module into text by performing voice recognition;
A lecture keyword input module for inputting keywords related to a specific subject of a lecture recorded by the lecture recording module;
A search module for searching by a keyword inputted into a lecture keyword input module in a web server and sorting the search results in order of the number of times a keyword is included in a web page;
A text collecting module configured to extract and store text posted on web pages up to a predetermined rank among web pages searched by the search module;
An associated word detection module for detecting a word repeated two or more times among the text collected by the text collecting module;
An error detection module detecting a word having no information among the texts converted into the text conversion module based on the part-of-speech information and the pronunciation information of each word in connection with a dictionary providing service;
A correction module for comparing the word detected by the error detection module with the word detected by the related word detection module and correcting the detected word in the related word detection module if it meets a specific condition, and marking it differently according to the specific condition; And
The correction module includes a user correction module that allows the user to check the modified parts with different marks, and to correct the text.
The fix module
If the initiality of the detected words in the error detection module matches the initiality of the words detected in the related word detection module, correct the detected words in the related word detection module, mark them,
If the initiality of the words detected by the error detection module matches the initiality of the words detected by the related word detection module, and if one or more neutrals match, the corrected words are corrected by the words detected by the related word detection module and marked differently,
If the initiality of the words detected in the error detection module matches the initiality of the words detected in the related word detection module, the one or more neutrals match, and the one or more finalities match, correct the words detected in the related word detection module. Lecture speech file text conversion system based on the lecture-related keywords, including other marking it. delete The method of claim 1,
The lesson keyword entry module
A weight can be given to each input keyword, and the search module calculates the number of keyword inclusions by applying weights when searching.
Lecture speech file text conversion system based on the lecture-related keywords, including.

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