CN113205717A - Deep learning-based oral English training method - Google Patents
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Abstract
The invention discloses an oral English training method based on deep learning, belonging to the field of English teaching, and comprising the following steps: (1) user registration and login and information protection; (2) judging the spoken language grade of the user; (3) setting a user desired level; (4) collecting information of capacity to be improved; (5) sorting the data in the database; (6) learning plan designation; (7) collecting and correcting defects of a user; (8) the spoken language grade of the user is updated regularly and a learning plan is made intelligently; the method and the device can orderly arrange the data stored in the database, reduce the probability of occurrence of unsmooth blocking when a user uses the device, improve the user experience, save the disk resources, save the time, intelligently modify the learning plan of the user, avoid the user changing the learning plan, improve the English spoken language improving efficiency of the user and save the labor.
Description
Technical Field
The invention relates to the field of English teaching, in particular to an English oral training method based on deep learning.
Background
The oral english language is a language form commonly used by people in english countries for oral communication, the oral english language is usually transmitted by sound, the oral english language is often written in English literary works, the oral english language is flexible and changeable, and is freely used due to different occasions and speakers, and in contrast to the oral english, written english is developed on the basis of oral language, and is used for written language expression, in china, the oral english learning is particularly important due to the gradual importance of practical english, wherein an immersed english learning method proposed by an english coach gradually becomes an english oral learning method widely accepted by the english training industry due to the practical angle, the language is originally a communication tool, and the oral communication is the most important and common communication method, so the importance of the oral english language is self-evident, however, the oral language is often not considered to be important in practical english teaching, the reasons are certainly many, firstly, the college entrance examination basically does not take oral languages, so that teachers and students do not pay attention to the teachers and the students, the oral languages are lacked, the language bases of the students are not solid, the hearing understanding is difficult to be realized, especially, the omission of the short-distance pronunciation words, the homophones, the Chinese phonetic streams and the like cannot be mastered, the development of the hearing is restricted due to the lacked oral languages, the knowledge plane of the students is narrow, the vocabularies, the syntax, the semantics, the social culture, the popular science knowledge and the like are deficient, a plurality of students are difficult to open, even generate 'oral language barriers', the students can lose the interest of learning English, lack the power and the capability of deep learning, and can not preliminarily establish an English thinking mode in a short time without the oral languages or lack of the oral languages, the students can not say that a firm and systematic English thinking mode is formed, but only depend on the communication of the mother languages to limit the reading understanding capability of the students, further restricting the effect of answering and completing shape filling, and learning spoken English often lacks methods, but lacks insistence and habits; therefore, it becomes especially important to invent a deep learning-based oral english training method;
through retrieval, Chinese patent No. CN107798927A discloses an oral English training system, although the invention can conveniently and effectively train and learn oral English, and correct mouth shape and pronunciation, the situation that the training files in the database are possibly blocked is extracted, and the user experience is reduced; the existing deep learning-based oral english training method cannot arrange data stored in a database in order, so that the situation that a user is stuck easily occurs when using the method, the occupancy rate of a communication channel is too large, the user experience is reduced, the magnetic disc resource is wasted, and the time is wasted; for this purpose, we propose an English spoken language training method based on deep learning.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides an English spoken language training method based on deep learning.
In order to achieve the purpose, the invention adopts the following technical scheme:
the deep learning-based oral English training method comprises the following specific steps:
(1) user registration login and information protection: the user inputs corresponding user information to register and log in, and simultaneously protects the user information;
(2) and (3) judging the spoken language grade of the user: the intelligent judgment is carried out by manually selecting the spoken language grade by a user or through testing;
(3) setting the user desired level: spoken language level settings that the user wishes to achieve;
(4) collecting information of capacity to be improved: collecting English ability information which a user wants to promote;
(5) sorting the data in the database: classifying the data in the database;
(6) learning plan designation: intelligently generating a proper learning plan and selecting the learning plan by a user;
(7) user defects are collected and corrected: receiving spoken language information of a user and feeding back spoken language defects of the user;
(8) the spoken language grade of the user is updated regularly and a learning plan is made intelligently: and intelligently judging the spoken language grade of the user regularly and updating the learning plan according to the grade change.
Further, the user inputs related user information on the spoken language training website or software in the step (1), the user information includes a user name, a user password, a mobile phone number and a user social account number, and meanwhile the background server encrypts and stores the user information.
Further, in the step (2), the spoken language rating of the user is judged and analyzed in two ways, namely, self-judgment selection or intelligent test selection, and the specific judgment and analysis steps are as follows:
the method comprises the following steps: dividing the spoken language into a grade 1 to a grade 7;
step two: the user self-judges and selects one grade from the grade 1 to the grade 7;
step three: if the user can not accurately judge by himself, the user grade is judged in an intelligent test mode, and the specific intelligent test steps are as follows:
the first step is as follows: the method comprises the following steps that a spoken language training website or software displays English sentences for testing to a user through intelligent equipment, wherein the intelligent equipment is one of a smart phone or a computer;
the second step is that: reading corresponding English sentences by the user, and collecting voice information generated by reading by the user and generating analysis data through data conversion by the intelligent equipment;
the third step: and analyzing and judging the analysis data, determining the spoken language grade of the user, and feeding back the judgment result to the user.
Further, in the step (3), the user formulates a target spoken language level according to the spoken language level of the user, and the specific formulation steps are as follows:
i, manually selecting a target spoken language grade required to be achieved by a user according to the spoken language grade of the user;
and II, intelligently analyzing the spoken language grades of the user and providing the optimal target spoken language grade for the user to be selected by the user.
Further, in step (4), the user inputs the english ability that the user wants to promote through an input device, wherein the input device includes one of a keyboard, a touch screen or an electronic pen, and the english ability includes spoken language, vocabulary, grammar and hearing.
Further, the data in the database in the step (5) is classified and labeled according to different grades, and the specific classification and labeling steps are as follows:
s1: classifying the training data in the database according to the grade 1 to the grade 7, and respectively marking the grades as A, B, C, D, E, F, G;
s2: the training data in A, B, C, D, E, F, G is classified by spoken language, vocabulary, grammar, and hearing.
Further, the learning plan in the step (6) is formulated and fed back to the user through intelligent analysis combining three aspects of the user spoken language level, the target spoken language level and the English ability which is expected to be improved, and the learning plan is selected and modified by the user.
Further, in the step (7), the spoken language information of the user is collected by a data collecting device and analyzed and determined, wherein the data collecting device is one of a microphone and a recorder, and the specific analyzing and determining steps are as follows:
SS 1: collecting spoken information of a user and generating comparison data through data conversion;
SS 2: extracting corresponding voice data stored in a database and converting the voice data into template data;
SS 3: and comparing and analyzing the comparison data and the template data, marking error parts in the comparison data and feeding back the error parts to the user, and providing modification opinions.
Further, in the step (8), the spoken language rating of the user is judged and updated by regularly collecting daily learning information of the user, and the specific judgment and update steps are as follows:
SSS 1: regularly collecting daily learning information of a user, wherein the learning information comprises a daily training error rate, a learning test score and a learning plan adherence date;
SSS 2: judging and evaluating the learning information of the user and updating the spoken language grade of the user again, wherein the specific judging and evaluating steps are as follows:
SSSS1, performing grade score judgment on the user learning information and marking the grade score as X;
the SSSS2, if X is 60, keeping the original grade of the user unchanged, and meanwhile, not needing to update the learning plan;
SSSS3, if X is less than 60, judging that the spoken language level of the user is reduced, and simultaneously reducing the difficulty of learning a plan;
and SSSS4, if X is more than 60, judging that the spoken language level of the user is improved, and simultaneously improving the difficulty of learning and planning.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the deep learning-based oral English training method, the spoken language grade of a user is judged and classified according to grade 1-grade 7 through intelligent equipment, wherein the intelligent equipment is one of a smart phone or a computer, the target setting is simultaneously carried out on the target spoken language grade of the user, the English ability which the user wants to improve is collected, after data collection is completed, training data in a database are classified according to different grades, and then are classified according to spoken language, vocabulary, grammar and hearing, data stored in the database are orderly arranged, the probability of occurrence of a blocking and pausing condition when the user uses the system is reduced, the user use experience is improved, disk resources are saved, and time is saved;
2. compared with the past single spoken language training, the deep learning-based oral English training method can regularly collect and analyze the daily learning information of the user, wherein the learning information of the user comprises a daily training error rate, a learning test score and a learning plan insisting date, and meanwhile, the spoken language grade of the user is judged again according to the daily learning information of the user, the learning plan of the user is intelligently modified, the user is not required to change the learning plan, the oral English promotion efficiency of the user is improved, and manpower is saved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a flow chart of the deep learning-based spoken english training method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Referring to fig. 1, the embodiment discloses an english spoken language training method based on deep learning, which includes the following specific steps:
(1) user registration login and information protection: the user inputs corresponding user information to register and log in, and simultaneously protects the user information;
the user inputs related user information on a spoken language training website or software, the user information comprises a user name, a user password, a mobile phone number and a user social account, and meanwhile the background server encrypts and stores the user information.
Specifically, in this embodiment, after applying for an access right to a user, a spoken training website or software starts to collect user information, when it is detected that the user logs in a third playing account, an encrypted code starts to be acquired, a network backend server carries the encrypted code information to access a third party account server, an intelligent application program calling right is acquired, associated third party login starts to be performed, a login registration link is simplified, better experience is provided for the user, after login is completed, a user mobile phone number is automatically retrieved and analyzed, the user is reminded of binding, and user time is saved.
(2) And (3) judging the spoken language grade of the user: the intelligent judgment is carried out by manually selecting the spoken language grade by a user or through testing;
specifically, in this embodiment, the spoken language level of the user is judged and analyzed by two ways, namely, self-judgment selection or intelligent test selection, and the specific judgment and analysis steps are as follows:
the method comprises the following steps: dividing the spoken language into a grade 1 to a grade 7;
step two: the user self-judges and selects one grade from the grade 1 to the grade 7;
step three: if the user can not accurately judge by himself, the user grade is judged in an intelligent test mode, and the specific intelligent test steps are as follows:
the first step is as follows: the method comprises the following steps that a spoken language training website or software displays English sentences for testing to a user through intelligent equipment, wherein the intelligent equipment is one of a smart phone or a computer;
the second step is that: reading corresponding English sentences by the user, and collecting voice information generated by reading by the user and generating analysis data through data conversion by the intelligent equipment;
the third step: and analyzing and judging the analysis data, determining the spoken language grade of the user, and feeding back the judgment result to the user.
Specifically, the method for determining spoken language grades disclosed in this embodiment captures a large amount of spoken language voice information in english from the internet, performs big data analysis and processing on the spoken language voice information, grades the data according to grades 1 to 7, sets a standard for each grade, and feeds the grade back to the user for self-selection or intelligent test, after the user performs self-selection on the spoken language grade, the user grade is uploaded to the cloud server for storage, and when the user selects the intelligent test, the spoken language training website or software displays English dialogs respectively representing different grades to a user through intelligent equipment, the user needs to read the English dialogs aloud, voice information generated by reading aloud of the user is processed through voice collection, feature extraction and grade matching, and meanwhile, determining the user grade, and uploading the test result to a cloud server for storage.
(3) Setting the user desired level: spoken language level settings that the user wishes to achieve;
the user formulates a target spoken language grade according to the spoken language grade of the user, and the specific formulation steps are as follows:
i, manually selecting a target spoken language grade required to be achieved by a user according to the spoken language grade of the user;
and II, intelligently analyzing the spoken language grades of the user and providing the optimal target spoken language grade for the user to be selected by the user.
(4) Collecting information of capacity to be improved: collecting English ability information which a user wants to promote;
specifically, a user inputs English ability which the user wants to improve through an input device, wherein the input device comprises one of a keyboard, a touch screen or an electronic pen, and the English ability comprises spoken language, words, grammar and hearing;
(5) sorting the data in the database: classifying the data in the database;
specifically, the data in the database disclosed in this embodiment is classified and labeled according to different grades, and the specific classification and labeling steps are as follows:
s1: classifying the training data in the database according to the grade 1 to the grade 7, and respectively marking the grades as A, B, C, D, E, F, G;
s2: the training data in A, B, C, D, E, F, G is classified by spoken language, vocabulary, grammar, and hearing.
(6) Learning plan designation: intelligently generating a proper learning plan and selecting the learning plan by a user;
specifically, the learning plan is made by combining three aspects of the spoken language level of the user, the target spoken language level and the English ability expected to be improved, and is fed back to the user, and the user selects and modifies the learning plan.
(7) User defects are collected and corrected: receiving spoken language information of a user and feeding back spoken language defects of the user;
specifically, the spoken language information of the user disclosed in this embodiment is collected by a data collection device, and analyzed and determined, where the data collection device is one of a microphone and a recorder, and the specific analyzing and determining steps are as follows:
SS 1: collecting spoken information of a user and generating comparison data through data conversion;
SS 2: extracting corresponding voice data stored in a database and converting the voice data into template data;
SS 3: and comparing and analyzing the comparison data and the template data, marking error parts in the comparison data and feeding back the error parts to the user, and providing modification opinions.
(8) The spoken language grade of the user is updated regularly and a learning plan is made intelligently: the spoken language grade of the user is intelligently judged at regular intervals, and the learning plan is updated according to grade change;
specifically, the spoken language rating of the user disclosed in this embodiment is determined and updated by periodically collecting daily learning information of the user and performing the following specific determination and update steps:
SSS 1: regularly collecting daily learning information of a user, wherein the learning information comprises a daily training error rate, a learning test score and a learning plan adherence date;
SSS 2: judging and evaluating the learning information of the user and updating the spoken language grade of the user again, wherein the specific judging and evaluating steps are as follows:
SSSS1, performing grade score judgment on the user learning information and marking the grade score as X;
the SSSS2, if X is 60, keeping the original grade of the user unchanged, and meanwhile, not needing to update the learning plan;
SSSS3, if X is less than 60, judging that the spoken language level of the user is reduced, and simultaneously reducing the difficulty of learning a plan;
and SSSS4, if X is more than 60, judging that the spoken language level of the user is improved, and simultaneously improving the difficulty of learning and planning.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (9)
1. The deep learning-based oral English training method is characterized by comprising the following specific steps:
(1) user registration login and information protection: the user inputs corresponding user information to register and log in, and simultaneously protects the user information;
(2) and (3) judging the spoken language grade of the user: the intelligent judgment is carried out by manually selecting the spoken language grade by a user or through testing;
(3) setting the user desired level: spoken language level settings that the user wishes to achieve;
(4) collecting information of capacity to be improved: collecting English ability information which a user wants to promote;
(5) sorting the data in the database: classifying the data in the database;
(6) learning plan designation: intelligently generating a proper learning plan and selecting the learning plan by a user;
(7) user defects are collected and corrected: receiving spoken language information of a user and feeding back spoken language defects of the user;
(8) the spoken language grade of the user is updated regularly and a learning plan is made intelligently: and intelligently judging the spoken language grade of the user regularly and updating the learning plan according to the grade change.
2. The oral english practice training method based on deep learning of claim 1, wherein in step (1), the user inputs the relevant user information on the oral practice training website or software, the user information includes a user name, a user password, a mobile phone number and a user social account number, and the background server encrypts and stores the user information.
3. The deep learning-based spoken english training method of claim 1, wherein the spoken user level in step (2) is determined and analyzed by self-determination or intelligent test, and the specific steps of determining and analyzing are as follows:
the method comprises the following steps: dividing the spoken language into a grade 1 to a grade 7;
step two: the user self-judges and selects one grade from the grade 1 to the grade 7;
step three: if the user can not accurately judge by himself, the user grade is judged in an intelligent test mode, and the specific intelligent test steps are as follows:
the first step is as follows: the method comprises the following steps that a spoken language training website or software displays English sentences for testing to a user through intelligent equipment, wherein the intelligent equipment is one of a smart phone or a computer;
the second step is that: reading corresponding English sentences by the user, and collecting voice information generated by reading by the user and generating analysis data through data conversion by the intelligent equipment;
the third step: and analyzing and judging the analysis data, determining the spoken language grade of the user, and feeding back the judgment result to the user.
4. The deep learning-based oral english training method according to claim 1, wherein the user in step (3) makes a target spoken language level according to his spoken language level, which comprises the following specific steps:
i, manually selecting a target spoken language grade required to be achieved by a user according to the spoken language grade of the user;
and II, intelligently analyzing the spoken language grades of the user and providing the optimal target spoken language grade for the user to be selected by the user.
5. The deep learning-based oral english training method according to claim 1, wherein the user inputs the english ability he or she wishes to promote through an input device in step (4), wherein the input device comprises one of a keyboard, a touch screen or an electronic pen, and the english ability comprises spoken language, vocabulary, grammar and hearing.
6. The deep learning-based oral english practice training method according to claim 1, wherein the data in the database in step (5) is classified and labeled according to different grades, and the specific classification and labeling steps are as follows:
s1: classifying the training data in the database according to the grade 1 to the grade 7, and respectively marking the grades as A, B, C, D, E, F, G;
s2: the training data in A, B, C, D, E, F, G is classified by spoken language, vocabulary, grammar, and hearing.
7. The oral english practice method based on deep learning of claim 1 wherein the learning plan in step (6) is formulated and fed back to the user by intelligent analysis combining the spoken language level of the user, the target spoken language level and the english ability desired to be enhanced, and the learning plan is selected and modified by the user.
8. The method for spoken english training based on deep learning of claim 1, wherein the spoken english information of the user in step (7) is collected and analyzed and determined by a data collecting device, wherein the data collecting device is one of a microphone and a recorder, and the specific analyzing and determining steps are as follows:
SS 1: collecting spoken information of a user and generating comparison data through data conversion;
SS 2: extracting corresponding voice data stored in a database and converting the voice data into template data;
SS 3: and comparing and analyzing the comparison data and the template data, marking error parts in the comparison data and feeding back the error parts to the user, and providing modification opinions.
9. The deep learning-based spoken english training method according to claim 1, wherein the spoken user level in step (8) is updated by periodically collecting daily learning information of the user and making a judgment on the spoken user level, and the specific judgment updating steps are as follows:
SSS 1: regularly collecting daily learning information of a user, wherein the learning information comprises a daily training error rate, a learning test score and a learning plan adherence date;
SSS 2: judging and evaluating the learning information of the user and updating the spoken language grade of the user again, wherein the specific judging and evaluating steps are as follows:
SSSS1, performing grade score judgment on the user learning information and marking the grade score as X;
the SSSS2, if X is 60, keeping the original grade of the user unchanged, and meanwhile, not needing to update the learning plan;
SSSS3, if X is less than 60, judging that the spoken language level of the user is reduced, and simultaneously reducing the difficulty of learning a plan;
and SSSS4, if X is more than 60, judging that the spoken language level of the user is improved, and simultaneously improving the difficulty of learning and planning.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113611172A (en) * | 2021-08-18 | 2021-11-05 | 江苏熙枫教育科技有限公司 | English listening comprehension training method based on deep learning |
| CN115116285A (en) * | 2022-06-21 | 2022-09-27 | 浪潮卓数大数据产业发展有限公司 | Online learning system based on WeChat native framework |
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