CN114327357B - Language learning assisting method, electronic equipment and storage medium - Google Patents
Language learning assisting method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN114327357B CN114327357B CN202210005436.4A CN202210005436A CN114327357B CN 114327357 B CN114327357 B CN 114327357B CN 202210005436 A CN202210005436 A CN 202210005436A CN 114327357 B CN114327357 B CN 114327357B
- Authority
- CN
- China
- Prior art keywords
- pronunciation
- phonemes
- student user
- student
- feature vector
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 239000000463 material Substances 0.000 claims abstract description 31
- 238000004590 computer program Methods 0.000 claims description 9
- 230000003993 interaction Effects 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 6
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 210000003928 nasal cavity Anatomy 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 210000001260 vocal cord Anatomy 0.000 description 1
Landscapes
- Electrically Operated Instructional Devices (AREA)
Abstract
The invention provides a language learning assisting method, electronic equipment and a storage medium. The method comprises the steps of obtaining errors between pronunciation of key words in electronic readings and standard pronunciation of the key words by student users recorded on site; the feedback parameters of each student user are determined, the current playing speech speed is adjusted according to the feedback parameters and the normal speaking speech speed of the student user in the age stage, the intelligentization and self-adaption of the playing speech speed when the student user learns the electronic reading material are realized, and the manual adjustment of the playing speech speed and repeated playing in the learning process are avoided.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a language learning support method, an electronic device, and a storage medium.
Background
At present, in the learning process according to the electronic reading material, when the playing speed is too fast or the pronunciation or pronunciation is not standard when the student follows the reading of the electronic reading material, the playing speed needs to be manually adjusted or repeated playing is continuously carried out, so that the student user can hear, and the user experience is poor. The electronic reading material playing process is not intelligent, personalized and self-adaptive enough for the adjustment of playing speech speed, and can not adapt to the individual demands of users.
Disclosure of Invention
The invention aims to solve the technical problem that in the prior art, the adjustment of the playing speed of an electronic reading material is not intelligent, personalized and self-adaptive enough, and provides a language learning auxiliary method, electronic equipment and a storage medium.
The invention solves the technical problems by the following technical scheme:
the invention provides a language learning auxiliary method, which comprises the following steps:
s1, acquiring a normal speaking speed of a student user at an age stage and a feedback parameter of the student user;
according to the face information of each student user, searching age stage and normal speaking speed and feedback parameters corresponding to the student user from a database; the method comprises the steps of storing age and face information corresponding to each student user and feedback parameters uniquely corresponding to the student in a database, and storing the trained normal speaking speed of each age stage; s2, determining the pushed electronic reading materials according to the history information and the preference of the student users;
the history information and the preference comprise the completion rate and the playing times of listening to the electronic reading material in a period of time; the pushed electronic reading materials comprise electronic reading materials related to the electronic reading materials with the highest completion rate and the largest playing times;
s3, determining the playing speech speed of the electronic reading materials pushed to the student users according to the normal speaking speech speed and the feedback parameters;
s4, playing the electronic reading material according to the playing language speed; judging whether the playing is finished, if so, executing S7;
s5, judging whether the student user and the electronic reading material enter an interaction stage, if so, executing S6; if not, returning to the step S4;
s6, after the current interaction stage is finished, acquiring errors between pronunciation of key words in the electronic readings and standard pronunciation of the key words by the student users recorded on site;
if the error is greater than the preset threshold, the error is used as the latest feedback parameter, and S3 is returned; if the error is not greater than the preset threshold, continuing to play the electronic reading material, and returning to the step S4;
s7, counting the completion rate of the electronic books of the student users and displaying the completion rate in the background; the play speech rate=normal speaking speech rate+a feedback parameter, wherein a is a preset coefficient, and the initial value of the feedback parameter is 0.
Preferably, the determining by the student user of the error between the pronunciation of the key word and the standard pronunciation of said key word in the electronic book comprises: dividing the standard pronunciation of the key word into a plurality of phonemes, and dividing the pronunciation of the student user aiming at the key word into a plurality of phonemes; and comparing errors between the phonemes of the standard pronunciation of the key words and the phonemes of the pronunciation of the key words in the electronic reading by the student user, and taking the errors as feedback parameters.
Preferably, comparing errors between the plurality of phonemes of the standard pronunciation of the key word and the plurality of phonemes of the pronunciation of the key word by the student user for the electronic book comprises: each phoneme uniquely corresponds to a feature vector, an error feature vector is formed according to errors between the feature vector of each phoneme in the plurality of phonemes of the standard pronunciation of the key word and the feature vector of each phoneme in the plurality of phonemes of the pronunciation of the key word by a student user, a phoneme error matrix of the key word is formed according to the error feature vector, the rank of the error matrix is calculated, and the rank is used as a feedback parameter.
Preferably, comparing errors between the plurality of phonemes of the standard pronunciation of the keyword with the plurality of phonemes of the pronunciation of the student user for the keyword comprises: each phoneme uniquely corresponds to a feature vector, the Euclidean distance between the feature vector of each phoneme in the plurality of phonemes of the standard pronunciation of the key word and the feature vector of each phoneme in the plurality of phonemes of the pronunciation of the key word by the student user is used as a feedback parameter, and the average value or the sum value of the obtained Euclidean distances corresponding to the plurality of phonemes divided by the key word is used as a feedback parameter.
Preferably, before step S1, the method further comprises an offline phase:
dividing a plurality of students into a plurality of age groups according to ages, and collecting a speech rate sample of normal speaking of the students in each age group;
according to the speech rate samples of the normal speaking of a plurality of students in each age stage, a clustering algorithm is adopted to obtain the normal speaking speech rate of each age stage; the clustering algorithm is a K-means algorithm.
The invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method when executing the computer program.
The present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
According to the invention, the feedback parameters of each student user are determined by acquiring the error between the pronunciation of the key words in the electronic reading material and the standard pronunciation of the key words, and the playing speed of the electronic reading material is adjusted in real time according to the feedback parameters, so that the automatic adjustment of the playing speed of the electronic reading material is realized, and the self-adaption and individuation of the playing speed are realized.
Drawings
Fig. 1 is a flowchart of a language learning support method in embodiment 1.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention.
Example 1
The present embodiment provides a language learning assisting method,
offline stage:
dividing a plurality of students into a plurality of age groups according to ages, and collecting a speech rate sample of normal speaking of the students in each age group;
according to the speech rate samples of the normal speaking of a plurality of students in each age stage, a clustering algorithm is adopted to obtain the normal speaking speech rate of each age stage; the clustering algorithm is a K-means algorithm.
On-line stage:
s1, acquiring a normal speaking speed of a student user at an age stage and a feedback parameter of the student user;
according to the face information of each student user, searching age stage and normal speaking speed and feedback parameters corresponding to the student user from a database; the method comprises the steps of storing age and face information corresponding to each student user and feedback parameters uniquely corresponding to the student in a database, and storing the trained normal speaking speed of each age stage;
s2, determining the pushed electronic reading materials according to the history information and the preference of the student users;
the history information and the preference comprise the completion rate and the playing times of listening to the electronic reading material in a period of time; the pushed electronic reading materials comprise electronic reading materials related to the electronic reading materials with the highest completion rate and the largest playing times;
s3, determining the playing speech speed of the electronic reading materials pushed to the student users according to the normal speaking speech speed and the feedback parameters;
s4, playing the electronic reading material according to the playing language speed; judging whether the playing is finished, if yes, executing S7;
s5, judging whether the student user and the electronic reading material enter an interaction stage, if so, executing S6; if not, returning to the step S4;
s6, after the current interaction stage is finished, acquiring errors between pronunciation of key words in the electronic readings and standard pronunciation of the key words by the student users recorded on site;
phonemes are the smallest phonetic units that are partitioned according to the natural properties of speech. Phonemes are the smallest phonetic units that are separated from the perspective of sound quality. From a physiological standpoint, a pronunciation action forms a phoneme. Phonemes are generally described in terms of pronunciation actions. The sound producing action of [ m ] is that the upper lip and the lower lip are closed, the vocal cords vibrate, and the air flow flows out from the nasal cavity to produce sound. If [ ma ] contains [ m ] [ a ], two pronunciation actions are two phonemes. The sounds made by the same pronunciation action are the same phonemes, and the sounds made by different pronunciation actions are different phonemes. In [ ma-mi ], two [ m ] pronunciation actions are the same and are the same phonemes, and [ a ] and [ i ] pronunciation actions are different and are different phonemes.
In this embodiment, the standard pronunciation of the key word is divided into a plurality of phonemes, and the pronunciation of the student user for the key word is divided into a plurality of phonemes; and comparing errors between the phonemes of the standard pronunciation of the key words and the phonemes of the pronunciation of the key words in the electronic reading by the student user, and taking the errors as feedback parameters.
In this embodiment, each phoneme uniquely corresponds to a feature vector, an error feature vector is formed according to an error between the feature vector of each phoneme in the plurality of phonemes of the standard pronunciation of the key word and the feature vector of each phoneme in the plurality of phonemes of the pronunciation of the key word by the student user, a phoneme error matrix of the key word is formed according to the error feature vector, and a rank of the error matrix is calculated, and the rank is used as a feedback parameter.
In this embodiment, comparing errors between a plurality of phonemes of a standard pronunciation of a keyword and a plurality of phonemes of a pronunciation of the keyword by a student user includes: each phoneme uniquely corresponds to a feature vector, the Euclidean distance between the feature vector of each phoneme in the plurality of phonemes of the standard pronunciation of the key word and the feature vector of each phoneme in the plurality of phonemes of the pronunciation of the key word by the student user is used as a feedback parameter, and the average value or the sum value of the obtained Euclidean distances corresponding to the plurality of phonemes divided by the key word is used as a feedback parameter.
If the error is greater than the preset threshold, the error is used as the latest feedback parameter, and S3 is returned; if the error is not greater than the preset threshold, continuing to play the electronic reading material, and returning to the step S4;
s7, counting the completion rate of the electronic books of the student users and displaying the completion rate in the background; the play speech rate=normal speaking speech rate+a feedback parameter, wherein a is a preset coefficient, and the initial value of the feedback parameter is 0.
Example 2
A language learning aid comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of embodiment 1 when executing the computer program.
Example 3
The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the language learning support method described in embodiment 1.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the language learning aid steps as described in embodiment 1, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (6)
1. A language learning auxiliary method is characterized in that:
s1, acquiring a normal speaking speed of a student user at an age stage and a feedback parameter of the student user;
s2, determining the pushed electronic reading materials according to the history information and the preference of the student users;
s3, determining the playing speech speed of the electronic reading materials pushed to the student users according to the normal speaking speech speed and the feedback parameters; the play speech rate=normal speaking speech rate+a feedback parameter, wherein a is a preset coefficient, and the initial value of the feedback parameter is 0;
s4, playing the electronic reading material according to the playing language speed; judging whether the playing is finished, if so, executing S7;
s5, judging whether the student user is in the interaction stage with the electronic reading material, if so, executing S6; if not, returning to the step S4;
s6, after the current interaction stage is finished, acquiring errors between pronunciation of key words in the electronic readings and standard pronunciation of the key words by the student users recorded on site;
if the error is greater than the preset threshold, the error is used as the latest feedback parameter, and S3 is returned; if the error is not greater than the preset threshold, continuing to play the electronic reading material, and returning to the step S4;
s7, counting the completion rate of the electronic books of the student users and displaying the completion rate in the background;
the determination of the error between the pronunciation of the key words and the standard pronunciation of the key words in the electronic reading by the student user comprises the following steps: dividing the standard pronunciation of the key word into a plurality of phonemes, and dividing the pronunciation of the student user aiming at the key word into a plurality of phonemes; and comparing errors between the phonemes of the standard pronunciation of the key words and the phonemes of the pronunciation of the key words in the electronic reading by the student user, and taking the errors as feedback parameters.
2. A language learning support method according to claim 1, wherein comparing errors between a plurality of phonemes of a standard pronunciation of a keyword with a plurality of phonemes of a pronunciation of the keyword in the electronic book by a student user comprises the steps of: each phoneme uniquely corresponds to a feature vector, an error feature vector is formed according to errors between the feature vector of each phoneme in the plurality of phonemes of the standard pronunciation of the key word and the feature vector of each phoneme in the plurality of phonemes of the pronunciation of the key word by a student user, a phoneme error matrix of the key word is formed according to the error feature vector, the rank of the error matrix is calculated, and the rank is used as a feedback parameter.
3. A language learning assistance method as claimed in claim 1, wherein comparing errors between a plurality of phonemes of a standard pronunciation of a keyword with a plurality of phonemes of a pronunciation of the keyword by a student user comprises the steps of: each phoneme uniquely corresponds to a feature vector, the Euclidean distance between the feature vector of each phoneme in the plurality of phonemes of the standard pronunciation of the key word and the feature vector of each phoneme in the plurality of phonemes of the pronunciation of the key word by the student user is used as a feedback parameter, and the average value or the sum value of the obtained Euclidean distances corresponding to the plurality of phonemes divided by the key word is used as a feedback parameter.
4. The language learning support method according to claim 1, characterized by further comprising, before step S1, the steps of:
dividing a plurality of students into a plurality of age groups according to ages, and collecting a speech rate sample of normal speaking of the students in each age group;
according to the speech rate samples of the normal speaking of a plurality of students in each age stage, a clustering algorithm is adopted to obtain the normal speaking speech rate of each age stage; the clustering algorithm is a K-means algorithm.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the language learning support method of any one of claims 1-4 when the computer program is executed by the processor.
6. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the language learning support method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210005436.4A CN114327357B (en) | 2022-01-05 | 2022-01-05 | Language learning assisting method, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210005436.4A CN114327357B (en) | 2022-01-05 | 2022-01-05 | Language learning assisting method, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114327357A CN114327357A (en) | 2022-04-12 |
| CN114327357B true CN114327357B (en) | 2024-02-02 |
Family
ID=81025638
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210005436.4A Active CN114327357B (en) | 2022-01-05 | 2022-01-05 | Language learning assisting method, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114327357B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1787070A (en) * | 2005-12-09 | 2006-06-14 | 北京凌声芯语音科技有限公司 | Chip upper system for language learner |
| CN107945788A (en) * | 2017-11-27 | 2018-04-20 | 桂林电子科技大学 | A kind of relevant Oral English Practice pronunciation error detection of text and quality score method |
| CN108961868A (en) * | 2018-08-14 | 2018-12-07 | 徐州工业职业技术学院 | A kind of College English sound word auxiliary memory device |
| CN110085261A (en) * | 2019-05-16 | 2019-08-02 | 上海流利说信息技术有限公司 | A kind of pronunciation correction method, apparatus, equipment and computer readable storage medium |
| CN112233649A (en) * | 2020-10-15 | 2021-01-15 | 安徽听见科技有限公司 | Method, device and equipment for dynamically synthesizing machine simultaneous interpretation output audio |
-
2022
- 2022-01-05 CN CN202210005436.4A patent/CN114327357B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1787070A (en) * | 2005-12-09 | 2006-06-14 | 北京凌声芯语音科技有限公司 | Chip upper system for language learner |
| CN107945788A (en) * | 2017-11-27 | 2018-04-20 | 桂林电子科技大学 | A kind of relevant Oral English Practice pronunciation error detection of text and quality score method |
| CN108961868A (en) * | 2018-08-14 | 2018-12-07 | 徐州工业职业技术学院 | A kind of College English sound word auxiliary memory device |
| CN110085261A (en) * | 2019-05-16 | 2019-08-02 | 上海流利说信息技术有限公司 | A kind of pronunciation correction method, apparatus, equipment and computer readable storage medium |
| CN112233649A (en) * | 2020-10-15 | 2021-01-15 | 安徽听见科技有限公司 | Method, device and equipment for dynamically synthesizing machine simultaneous interpretation output audio |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114327357A (en) | 2022-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110148427A (en) | Audio-frequency processing method, device, system, storage medium, terminal and server | |
| CN106463113B (en) | Predicting pronunciation in speech recognition | |
| US8019602B2 (en) | Automatic speech recognition learning using user corrections | |
| JP3520022B2 (en) | Foreign language learning device, foreign language learning method and medium | |
| CN106531185B (en) | voice evaluation method and system based on voice similarity | |
| US10832668B1 (en) | Dynamic speech processing | |
| US10930264B2 (en) | Voice quality preference learning device, voice quality preference learning method, and computer program product | |
| JP2007249212A (en) | Method, computer program and processor for text speech synthesis | |
| US10515637B1 (en) | Dynamic speech processing | |
| US11676572B2 (en) | Instantaneous learning in text-to-speech during dialog | |
| JP2021501912A (en) | Filtering model training method and speech recognition method | |
| CN104900231A (en) | VOICE SEARCH DEVICE and VOICE SEARCH METHOD | |
| CN114783424A (en) | Text corpus screening method, device, equipment and storage medium | |
| US11915683B2 (en) | Voice adaptation using synthetic speech processing | |
| CN117711444B (en) | Interaction method, device, equipment and storage medium based on talent expression | |
| CN100495535C (en) | Speech recognition device and speech recognition method | |
| CN114327357B (en) | Language learning assisting method, electronic equipment and storage medium | |
| CN107910005A (en) | The target service localization method and device of interaction text | |
| JP7376071B2 (en) | Computer program, pronunciation learning support method, and pronunciation learning support device | |
| JP6027754B2 (en) | Adaptation device, speech recognition device, and program thereof | |
| JP2004117503A (en) | Method, device, and program for generating acoustic model for voice recognition, recording medium, and voice recognition device using the acoustic model | |
| CN111078937B (en) | Voice information retrieval method, device, equipment and computer readable storage medium | |
| Jin | Speech synthesis for text-based editing of audio narration | |
| Kessens et al. | On automatic phonetic transcription quality: lower word error rates do not guarantee better transcriptions | |
| Rojc et al. | Gradient-descent based unit-selection optimization algorithm used for corpus-based text-to-speech synthesis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |