CN116523371A - Teaching language specification level analysis method, system, device and medium - Google Patents
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Abstract
The invention provides a teaching language specification level analysis method, a system, a device and a medium, wherein the method comprises the following steps: acquiring an audio material through audio acquisition, and aligning the voice volume and the voice tone in the audio material according to a time base line of the audio material; performing text conversion on the aligned audio materials to obtain text materials, and classifying teaching language basic data in the text materials; determining voice index parameters corresponding to the classification result; outputting a teaching language specification level analysis result according to the voice index parameters; the scheme can realize objective diagnosis of teaching language standard level of the lecturer, provides scientific and accurate improvement suggestion for the lecturer, further improves teaching language standard level of the lecturer, and can be widely applied to relevant technical fields such as education technology, computer science and the like.
Description
Technical Field
The invention relates to the technical fields of education technology, computer science and the like, and in particular relates to a method, a system, a device and a medium for analyzing teaching language specification level.
Background
The teaching language is a main mode for transmitting teaching information, and the standardized teaching language has the positive effects of improving teaching quality, attracting the attention of students and the like. The well-known textist quarmeinius has stated that: "teacher's mouth is a source spring from which a stream of knowledge can be sent. Therefore, how to diagnose the teaching language standard level of the lecturer by using the language as a main carrier for transmitting teaching information is an important guarantee for improving the teaching efficiency of the classroom and promoting the development of the high quality of teaching.
The existing speech recognition technology can realize text conversion and audio feature extraction of the speech and is used for extraction and recognition of teaching contents and diagnosis of standard level of Mandarin, but cannot analyze and diagnose sound volume rationality, speech speed rationality, intonation rationality and the like in teaching languages, and cannot propose a systematic method for diagnosing teaching language normative level. The current level of teaching language specifications of diagnostic lecturers is dependent only on humans, which is highly subjective. In addition, the evaluation criteria of each diagnostician vary, and it is difficult to ensure the scientificity and objectivity of diagnosis.
Disclosure of Invention
Therefore, the invention aims to overcome the defects and shortcomings of the conventional teaching language standard level diagnosis of the lecturer, and provides a teaching language standard level analysis method based on a language identification technology and related parameters thereof, which can realize quantitative evaluation of each language skill item of the lecturer and simultaneously realize the judgment of the teaching language expression standard achievement degree of the lecturer in a boosting manner; the technical scheme of the application also provides a system, a device and a medium corresponding to the method.
On one hand, the technical scheme of the application provides a teaching language specification level analysis method, which comprises the following steps:
acquiring an audio material through audio acquisition, and aligning the voice volume and the voice tone in the audio material according to a time base line of the audio material;
performing text conversion on the aligned audio materials to obtain text materials, and classifying teaching language basic data in the text materials;
determining a voice index parameter corresponding to the classification result, wherein the voice index parameter comprises at least one of the following: spoken Buddhist frequency, volume rationality, speech speed rationality, intonation rationality, word distribution characteristics, sentence emotion polarity, and class question rate;
and outputting a teaching language specification level analysis result according to the voice index parameters.
In a possible embodiment of the present application, the determining a voice index parameter corresponding to the classification result includes:
and carrying out word frequency statistics on the character strings in the classification result, and determining the oral Buddha frequency according to the word frequency statistics result.
In a possible embodiment of the present application, the determining a voice index parameter corresponding to the classification result includes:
calculating the overall volume value of the audio material corresponding to the classification result and the volume change frequency value;
determining the volume rationality according to the volume overall size score and the volume change frequency score;
the calculation formula of the volume overall size score is as follows:
wherein,,to be the volume overall size score, x 1 For teaching language in given time tMidrange score, sigma 1 Is x 1 Weight scale of (a);
the calculation formula of the volume change times score is as follows:
wherein,,for the number of volume changes score, y 1 For the number of pitch changes score, σ, in the teaching language at a given time t 2 Is y 1 Weight scale of (a);
the calculation formula of the volume rationality is as follows:
wherein RoV is used to characterize volume rationality.
In a possible embodiment of the present application, the calculation formula of the speech speed rationality is as follows:
wherein, the RoS is used for describing the rationality of the speed of the language,COUNT is a statistical function of the number and Char is the character in the teaching language at a given time t; sigma (sigma) 3 Is a weight scale for speech rate.
In a possible embodiment of the present application, the calculation formula of the intonation rationality is as follows:
wherein RoI is used to describe the rationality of intonation,for describing intonation distribution values,/->Total number of times for describing intonation changes, x 3 The method comprises the steps of distributing numerical values for intonation in a teaching language at a given time t; y is 3 The number of intonation changes in the teaching language in a given time t; sigma (sigma) 4 Is x 3 Weight scale, sigma of 5 Is y 3 Is a weight scale of (a).
In a possible embodiment of the present application, the calculation formula of the emotion polarity of the sentence is as follows:
Ω k ＝U T (I k -m),k＝1,2,…,M
J k ＝UΩ k +m,k＝1,2,…,M
wherein α=1 represents a positive emotional state, α=2 represents a neutral emotional state, α=2 represents a negative emotional state, and α=3 represents a neutral emotional stateA state; poE (Power over Ethernet) α Sentence emotion polarity of the text materials in the classification result; c is the overall variance matrix of the intonation sample set represented by the N-dimensional vector I in the original space O; n is determined according to the standardized sampling point number of the intonation length; i i Is the ith intonation sample vector; m is the total number of all intonation samples; m is the average value of the intonation sample set, U is the feature vector set formed by arranging the feature values obtained by C solutions in descending order; omega shape k Projection of intonation subspace P, J, formed for U k For original intonation sample I k Based on projection omega k (k=1, 2, …, M) and the estimated value obtained by the feature vector set U, the N-dimensional vector extracts the value of the intonation extracted from the speech with the emotion state of α in the original intonation space O, Ω α Is I α Projection of the constructed subspace P;representing the projection vector of the kth intonation of emotion category α in space P.
In a possible embodiment of the present application, the calculation formula of the class question ratio is as follows:
wherein RoQ is the class question ratio, q is the sentence of the teacher in the teaching language, and NUM is the total sentence number of the teacher in the teaching language.
On the other hand, the technical scheme of the application also provides a teaching language specification level analysis system, which comprises:
the audio alignment unit is used for acquiring audio materials through audio acquisition and aligning the voice volume and the voice tone in the audio materials according to the time base line of the audio materials;
the voice conversion unit is used for carrying out text conversion on the aligned audio materials to obtain text materials and classifying teaching language basic data in the text materials;
the parameter calculation unit is used for determining a voice index parameter corresponding to the classification result, wherein the voice index parameter comprises at least one of the following components: spoken Buddhist frequency, volume rationality, speech speed rationality, intonation rationality, word distribution characteristics, sentence emotion polarity, and class question rate;
and the analysis processing unit is used for outputting the result of the teaching language specification level analysis according to the voice index parameters.
On the other hand, this application technical scheme still provides teaching language specification level analysis device, and this device includes:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to perform the teaching language specification level analysis method of any one of the first aspects.
In another aspect, the present application further provides a storage medium, in which a processor-executable program is stored, where the processor-executable program is used to perform the teaching language specification level analysis method according to any one of the first aspects when executed by a processor.
Advantages and benefits of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention:
according to the teaching language specification level analysis method, system, device and medium, the automatic analysis of the spoken Buddha frequency, the sound volume rationality, the speech speed rationality, the speech rationality, the word distribution characteristics, the sentence emotion polarity and the class question ratio and seven index parameters is realized in a visual mode through parameters such as the sound volume distribution value, the sound volume change times, the intonation distribution value and the intonation change times based on the technologies such as voice recognition, word part-of-speech judgment and emotion polarity judgment, so that the teaching language specification level of an objective diagnosis lecturer is realized, scientific and accurate improvement suggestions are provided for the lecturer, and the teaching language specification level of the lecturer is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of a method for analyzing teaching language specification level provided in the technical scheme of the present application;
fig. 2 is a summary diagram of the frequency FoC of oral Buddhist in the present application;
fig. 3 is an analysis chart of volume rationality RoV in the technical solution of the present application;
FIG. 4 is an analysis chart of the speech rate rationality Ross and the speech rate rationality Ros in the technical solution of the present application;
FIG. 5 is an analysis chart of word distribution SC and sentence emotion polarity PoE in the technical scheme of the application;
fig. 6 is a graph of a classroom interaction time distribution in the technical solution of the present application.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
The main purpose of the technical scheme is to realize automatic analysis and result visualization of seven index parameters of spoken Buddhist frequency, volume rationality, language speed rationality, language adjustment rationality, word distribution characteristics, sentence emotion polarity and class question ratio by truly recording and processing teaching language data of the lecturer, comprehensively diagnose teaching language standard level of each lecturer, realize quantitative evaluation of each language skill item of the lecturer, and realize judgment of teaching language expression standard achievement degree of the lecturer by assistance. In order to achieve the above objective, the technical solution of the present application firstly provides a method for analyzing teaching language specification level, which may include steps S01-S04:
s01, acquiring an audio material through audio acquisition, and aligning the voice volume and the voice tone in the audio material according to a time base line of the audio material.
In an embodiment, the voice acquisition equipment acquires the whole process of audio acquisition, and uploads voice data to the cloud application interface through a network, and alignment of voice volume and tone based on a time base line is automatically achieved in real time.
S02, performing text conversion on the aligned audio materials to obtain text materials, and classifying teaching language basic data in the text materials.
In an embodiment, text conversion is performed on the audio material after the alignment processing in step S01, so as to obtain a corresponding text material. After completing the conversion from the voice material to the text material, the embodiment may further perform classification processing on the obtained text material; in an exemplary embodiment, the text material of each target object is finally obtained by performing classification processing according to different target objects, and simultaneously classifying the audio material corresponding to the text material into a material cluster of the same target object.
S03, determining voice index parameters corresponding to the classification result; wherein the voice index parameter comprises at least one of the following: spoken Buddhist frequency (FoC, frequency of catchphrases), volume rationality (RoV, rationality of volume), speech speed rationality (RoS, rationality of speech speed), intonation rationality (RoI, rationality of intonation), word distribution characteristics SC, sentence emotion polarity (PoE, polarity of statement emotion), and class question-and-answer ratio (RoQ, rate of classroom questions).
In an embodiment, voice index parameters reflecting teaching language specification level are calculated, wherein the voice index parameters comprise FoC, roV, roS, roI, SC, poE and RoQ parameters respectively representing spoken Buddhist frequency, volume rationality, speed rationality, intonation rationality, word distribution characteristics, sentence emotion polarity and class question rate.
In some possible embodiments, the step S03 of determining the voice index parameter corresponding to the classification result in the method may include step S031 of performing word frequency statistics on the character strings in the classification result, and determining the spoken Buddha frequency according to the result of the word frequency statistics.
The words with ten top ranking frequency of the word (namely oral Buddhist) which frequently appears in the teaching process of the teaching person are reflected, and accordingly the specification of language expression in the teaching process of the teaching person is judged. In an embodiment, the frequency FoC of the spoken Buddhist is characterized by the occurrence frequency of the spoken Buddhist in the teaching language, and the method for calculating the frequency of the spoken Buddhist is as follows:
wherein, string i For i characters/strings, COUNT is a statistical function of the number.
Illustratively, as shown in fig. 2, the specification of language expressions in the teaching of the lecturer is determined by reflecting the ten-top words of the total frequency of occurrence of a word frequently occurring in the teaching of the lecturer (i.e., spoken Buddhist). In the embodiment shown in figure 2 of the drawings, the frequent people of the oral Buddhist TOP10 in the teaching process are ' prayer ', ' then ' is ' good ', ' care ', ' lifting hands ', ' good ', ' people ' and ' prayer ', ' is ' prayer ', therefore, according to the situation, the lecturer can be given corresponding advice in a targeted way, namely, part of the oral Buddhist affects the lecture efficiency of the students, is unfavorable for the students to develop good language habits, and can take more attention after adjusting and improving.
In some possible embodiments, the step S03 of determining the voice index parameter corresponding to the classification result in the method may further include step S032 of calculating a volume overall size score and a volume change frequency score of the audio material corresponding to the classification result, and determining the volume rationality according to the volume overall size score and the volume change frequency score.
And features of the volume and the volume conversion frequency in the teaching process of the lecturer are reflected, namely the standard level of the volume and the volume conversion frequency in the teaching process of the lecturer is determined according to the volume and the volume conversion frequency. In an embodiment, the volume rationality RoV is characterized by a comprehensive score of the overall volume size and the number of volume changes in the teaching language, and the volume rationality is calculated according to the following formula:
wherein RoV is used to characterize the volume rationality,to be the volume overall size score, x 1 For the volume score, σ, in the teaching language at a given time t 1 Is x 1 Weight scale of (a); />For the number of volume changes score, y 1 For the number of pitch changes score, σ, in the teaching language at a given time t 2 Is y 1 Is a weight scale of (a). In an embodiment, sigma can be calculated according to the actual situation 1 Sum sigma 2 The two weight scales are adjusted, and the value 50 in the calculation formula is the optimal volume overall size, and the value 2 in the calculation formula is the optimal volume change times.
Illustratively, as shown in fig. 3, features of both the volume level and the volume transition frequency in the teaching of the lecturer are reflected, that is, the specification level of both the volume level and the volume transition frequency in the teaching of the lecturer is determined according to the volume level and the volume transition frequency. In the embodiment of fig. 3, the volume of the first 3 minutes is low overall and the volume of the 8 minutes to 9 minutes is high overall during the teaching, but the overall volume during the teaching ranges from 65 db to 77 db, and the volume is moderate overall; the volume transition frequency is 3.6 times/min, which is generally appropriate. In the teaching process, reasonable volume conversion is helpful for expressing emotion and improving attention.
In some possible embodiments, the step S03 of determining the voice index parameter corresponding to the classification result in the method may further include step S033 of calculating the speech speed rationality of the voice material.
The characteristics of the two aspects of the speed and the intonation of the lecturer in the lecture process are reflected, namely the standard level of the two aspects of the speed and the intonation of the lecturer in the lecture process is judged according to the speed, the intonation level and the intonation change times. In an embodiment, the reasonable Ros of speech speed is characterized by a comprehensive score of speech speed in a teaching language, and the reasonable calculation formula of speech speed is as follows:
the RoS is used for describing the rationality of the speed of the language, v is the speed of the language, and Char is the character in the teaching language in a given time t; sigma (sigma) 3 Is a weight scale for speech rate. In an embodiment, the weight scale σ 3 The value 255 in the calculation formula is the optimal speech rate, which can be adjusted according to the specific implementation.
In some possible embodiments, the step S03 of determining the voice index parameter corresponding to the classification result in the method may further include step S304 of calculating the intonation rationality of the voice material.
In an embodiment, the intonation rationality RoI is characterized by a comprehensive score of intonation distribution values and intonation variation times in the teaching language, and the intonation rationality is calculated as follows:
wherein RoI is used to describe the rationality of intonation,for describing intonationDistribution value->Total number of times for describing intonation changes, x 3 The method comprises the steps of distributing numerical values for intonation in a teaching language at a given time t; y is 3 The number of intonation changes in the teaching language in a given time t; sigma (sigma) 4 Is x 3 Weight scale, sigma of 5 Is y 3 Is a weight scale of (a). Wherein sigma 4 Sum sigma 5 The two weight scales can be adjusted according to the specific implementation, wherein 237 is the best intonation and 2 is the best number of intonation changes.
Illustratively, as shown in fig. 4, features of both the speech speed and the intonation in the teaching of the lecturer are reflected, i.e., the normative level of both the speech speed and the intonation in the teaching of the lecturer is determined according to the magnitude of the speech speed, the level of the intonation, and the number of intonation changes. In the embodiment shown in fig. 4, the speech rate is faster between 2 minutes and 3 minutes and between 13 minutes and 14 minutes during the teaching process; the intonation range is 270Hz-360Hz, and the intonation overall is higher; the speech speed range is 253 words/min-260 words/min, and the overall speech speed is faster. The following suggestions are therefore made to the lecturer: (1) the tone is high, the emotion dysphoria is easy to cause, and the proper adjustment is recommended; (2) the tone changes are too frequent and abundant, and the tone is recommended to be adjusted according to the teaching content and design; (3) too fast speech rate can lead to the omission of information for students, please practice according to your current practice, and adjust the speech rate.
In some possible embodiments, the step S03 of determining the voice index parameter corresponding to the classification result in the method may further include step S305 of determining a word distribution feature in the text material.
The word distribution characteristics SC in the embodiment are characterized by the ratio of nouns, verbs and adjectives in the teaching language, which determines the word distribution characteristics of the teacher language content, and the calculation formula of the statement understandability is as follows:
pr is a part-of-speech ratio in a teaching language in a given sentence; pr (Pr) n Is noun part-of-speech word, pr v As verb part-of-speech words, prad is adjective part-of-speech words; NUM is the total number of part-of-speech divisions of a sentence.
In some possible embodiments, the step S03 of determining the voice index parameter corresponding to the classification result in the method may further include step S306 of calculating as the emotion polarity of the sentence in the material.
In particular, in the embodiment, the characteristic of the emotion polarity PoE of the sentence is emotion of the teaching language, that is, whether the language is positive, negative or neutral, and the calculation formula of the emotion polarity of the sentence is as follows:
Ω k ＝U T (I k -m),k＝1,2,…,M
J k ＝UΩ k +m,k＝1,2,…,M
wherein α=1 represents positive emotional state, α=2 tableShowing neutral emotional states, α=2 representing passive emotional states, α=3 representing neutral emotional states; poE (Power over Ethernet) α Sentence emotion polarity of the text materials in the classification result; c is the overall variance matrix of the intonation sample set represented by the N-dimensional vector I in the original space O; n is determined according to the standardized sampling point number of the intonation length; i i Is the ith intonation sample vector; m is the total number of all intonation samples; m is the average value of the intonation sample set, U is the feature vector set formed by arranging the feature values obtained by C solutions in descending order; omega shape k Projection of intonation subspace P, J, formed for U k For original intonation sample I k Based on projection omega k (k=1, 2, …, M) and the estimated value obtained by the feature vector set U, the N-dimensional vector extracts the value of the intonation extracted from the speech with the emotion state of α in the original intonation space O, Ω α Is I α Projection of the constructed subspace P;representing the projection vector of the kth intonation of emotion category α in space P.
Illustratively, as shown in fig. 5, features of both the understandability of the sentence and the emotion polarity of the sentence in the teaching of the lecturer are reflected, namely, the understandability of the sentence and the emotion polarity of the sentence are calculated and analyzed according to the duty ratio of nouns, verbs and adjectives in the sentence, so that the standardization level of the teaching language in the teaching of the lecturer is judged. In the embodiment shown in fig. 5, the noun accounts for 72.6%, the verb accounts for 23.2%, the adjective accounts for 2.1% and the other accounts for 2.1% in the teaching process; the emotion polarity of the total sentence of the teaching is positive. The following suggestions can thus be made: (1) the language content is mostly knowledge statement and explanation, mainly explaining principle and facts content, and less interaction or learning organization with students; (2) the knowledge explains too much and is unfavorable for the understanding of students, and the proposal is to attempt to adjust the teaching mode and enrich the interaction of teachers and students with language; (3) the positive emotion words are beneficial to motivating students to learn, and keep the emotion input state in teaching.
In some possible embodiments, the step S03 of determining the voice index parameter corresponding to the classification result in the method may further include step S307, calculating a class question ratio in the voice material or the text material.
In an embodiment, the classroom question rate RoQ is characterized by a rate of teacher questions in a teaching language, and a calculation formula of the teacher question rate is as follows:
wherein RoQ is the class question ratio, q is the sentence of the teacher in the teaching language, and NUM is the total sentence number of the teacher in the teaching language.
For example, as shown in fig. 6, the number of questions and the concentration time period in the teaching of the lecturer are reflected, that is, the class question rate is calculated by the number of questions in the teaching of the lecturer, and the standard level of the lecture language in the teaching of the lecturer is determined. In the embodiment shown in fig. 6, the number of questions during the teaching is 5, but all are concentrated in the period in which teaching starts. Suggestions can therefore be made to the lecturer: note that during teaching, students interact with questions.
S04, outputting a teaching language specification level analysis result according to the voice index parameters.
Illustratively, the specific analysis process (canonical evaluation annotation) in the embodiment includes:
(1) The frequency FoC of the oral Buddhist takes 4 times/min as the lowest standard, when the frequency exceeds a reasonable range value, the word or the character is automatically identified and diagnosed as the oral Buddhist;
(2) The sound volume distribution in the sound volume rationality RoV takes 40 to 60 dB as a reasonable range value, and 50 dB as the optimal overall volume size; the sound volume change times are in a reasonable range from 1 time to 3 times, and the sound volume change times are in the optimal sound volume change times from 2 times, wherein the sound volume rationality comprehensive score is lower than 60 minutes, the sound volume is automatically identified and the sound volume is diagnosed as not having rationality;
(3) The reasonable rate Ross takes 253-260 words/min as a reasonable range value, 255 words/min as the optimal rate, and if the optimal rate exceeds the reasonable range value, the reasonable rate Ross is automatically identified and the rate is diagnosed as not having rationality;
(4) The intonation distribution in the intonation rationality RoI takes 237mel-409mel bit reasonable range value and 323mel as the optimal intonation value; the number of intonation changes from 1 to 3 is a reasonable range, wherein the intonation rationality comprehensive score is lower than 60 points, the identification is automatically carried out after identification, and the intonation is diagnosed to be not rational;
(5) The word distribution SC takes the range of more than or equal to 2 percent and less than or equal to 20 percent as a standard, and when the word distribution SC exceeds a reasonable range, the word distribution SC automatically identifies the distribution situation of various parts of speech;
(6) Statement emotion polarity PoE is identified after automatic identification by taking positive emotion polarity as optimal polarity and non-positive emotion polarity, and statement emotion polarity is diagnosed as bad;
(7) And when the class question rate RoQ exceeds a reasonable range and exceeds the reasonable range, automatically identifying and then marking, and diagnosing that the class question rate is poor, wherein the class question rate RoQ is in a range of more than or equal to 22% and less than or equal to 30%.
On the other hand, the technical scheme of the application also provides a teaching language specification level analysis system, which comprises:
the audio alignment unit is used for acquiring audio materials through audio acquisition and aligning the voice volume and the voice tone in the audio materials according to the time base line of the audio materials;
the voice conversion unit is used for carrying out text conversion on the aligned audio materials to obtain text materials and classifying teaching language basic data in the text materials;
the parameter calculation unit is used for determining a voice index parameter corresponding to the classification result, wherein the voice index parameter comprises at least one of the following components: spoken Buddhist frequency, volume rationality, speech speed rationality, intonation rationality, word distribution characteristics, sentence emotion polarity, and class question rate;
and the analysis processing unit is used for outputting the result of the teaching language specification level analysis according to the voice index parameters.
On the other hand, this application technical scheme still provides teaching language specification level analysis device, and this device includes: at least one processor; at least one memory for storing at least one program; the at least one program, when executed by the at least one processor, causes the at least one processor to perform the teaching language specification level analysis method as described in the first aspect.
The embodiment of the invention also provides a storage medium which stores a corresponding execution program, and the program is executed by a processor to realize the teaching language specification level analysis method in the first aspect.
From the above specific implementation process, it can be summarized that, compared with the prior art, the technical solution provided by the present invention has the following advantages or advantages:
1. the technical scheme of the application provides a technical method for judging the teaching language standard level of the lecturer by utilizing the voice recognition technology, the language standard level of the lecturer in the teaching process can be judged through voice indexes and parameters, the language behaviors and data of the lecturer in the simulation teaching or real teaching process can be truly recorded by means of the voice recognition technology and platform tools, the assessment at any time and any place can be realized, and the objectivity and the normalization of the assessment are ensured;
2. the technical scheme provides seven key voice index parameters for reflecting teaching language standard level of a lecturer, wherein the seven key voice index parameters comprise spoken Buddhist frequency, volume rationality, speed rationality, intonation rationality, word distribution, sentence emotion polarity and class question-giving rate;
3. the technical scheme of the application provides a diagnosis standard for comprehensive analysis based on FoC, roV, roS, roI, SC, poE and RoQ seven voice index parameters;
4. according to the technical scheme, data acquisition and analysis of the teaching language specification level can be dynamically performed in real time, the past teaching language specification level is often subjected to subjective evaluation on site by a class listening expert and is influenced by personal subjective and objective environments, the authenticity and objectivity of an evaluation result are difficult to guarantee, and certain hysteresis exists. The real-time teaching language standard level condition in the teaching process of the lecturer cannot be truly reflected. Based on a voice recognition technology, a lecturer can record and store a sentence of voice into analysis software by voice equipment every time in the simulated lecture or real lecture process, so that the effectiveness and the authenticity of data in the lecture process of the lecturer are ensured;
5. in the technical scheme, a constant modulus value is not required to be determined in the teaching language specification level evaluation process. The invention is based on the speech recognition technology and the index parameter test teaching language standard level is different from the traditional paper pen test, the measurement is carried out after the constant modulus value is determined through large-scale data, but the progressive test is reflected by the teaching language standard level in the range of all the tested lecturers, so the test result of the method is dynamically changed, and the test result has a reference value along with the increase of the number of tested people. This is a key reason that the method is simple but can be widely applied.
Furthermore, while the invention is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the functions and/or features may be integrated in a single physical device and/or software module or may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the invention as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the invention, which is to be defined in the appended claims and their full scope of equivalents.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these equivalent modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (10)
1. The teaching language specification level analysis method is characterized by comprising the following steps of:
acquiring an audio material through audio acquisition, and aligning the voice volume and the voice tone in the audio material according to a time base line of the audio material;
performing text conversion on the aligned audio materials to obtain text materials, and classifying teaching language basic data in the text materials;
determining a voice index parameter corresponding to the classification result, wherein the voice index parameter comprises at least one of the following: spoken Buddhist frequency, volume rationality, speech speed rationality, intonation rationality, word distribution characteristics, sentence emotion polarity, and class question rate;
and outputting a teaching language specification level analysis result according to the voice index parameters.
2. The method for analyzing the specification level of a teaching language according to claim 1, wherein determining the voice index parameter corresponding to the classification result comprises:
and carrying out word frequency statistics on the character strings in the classification result, and determining the oral Buddha frequency according to the word frequency statistics result.
3. The method for analyzing the specification level of a teaching language according to claim 1, wherein determining the voice index parameter corresponding to the classification result comprises:
calculating the overall volume value of the audio material corresponding to the classification result and the volume change frequency value;
determining the volume rationality according to the volume overall size score and the volume change frequency score;
the calculation formula of the volume overall size score is as follows:
wherein,,to be the volume overall size score, x 1 For the volume score, σ, in the teaching language at a given time t 1 Is x 1 Weight scale of (a);
the calculation formula of the volume change times score is as follows:
wherein,,for the number of volume changes score, y 1 For the number of voice changes in the teaching language at a given time t,
σ 2 is y 1 Weight scale of (a);
the calculation formula of the volume rationality is as follows:
wherein RoV is used to characterize volume rationality.
4. The teaching language specification level analysis method according to claim 1, wherein the calculation formula of the speed rationality is as follows:
wherein, the RoS is used for describing the rationality of the speed of the language,v is the speech rate, COUNT is a statistical function of the number, char is the character in the teaching language at a given time t; sigma (sigma) 3 Is a weight scale for speech rate.
5. The method for analyzing teaching language specification level according to claim 1, wherein the calculation formula of the rationality of the language is as follows:
wherein RoI is used to describe the rationality of intonation,for describing intonation distribution values,/->Total number of times for describing intonation changes, x 3 The method comprises the steps of distributing numerical values for intonation in a teaching language at a given time t; y is 3 The number of intonation changes in the teaching language in a given time t; sigma (sigma) 4 Is x 3 Weight scale, sigma of 5 Is y 3 Is a weight scale of (a).
6. The method for analyzing the specification level of a teaching language according to claim 1, wherein the statement emotion polarity is calculated as follows:
Ω k ＝U T (I k -m),k＝1,2,…,M
J k ＝UΩ k +m,k＝1,2,…,M
wherein α=1 represents a positive emotional state, α=2 represents a neutral emotional state, α=2 represents a negative emotional state, and α=3 represents a neutral emotional state; poE (Power over Ethernet) α Sentence emotion polarity of the text materials in the classification result; c is the overall variance matrix of the intonation sample set represented by the N-dimensional vector I in the original space O; n is determined according to the standardized sampling point number of the intonation length; i.e i Is the ith intonation sample vector; m is the total number of all intonation samples; m is the average value of the intonation sample set, U is the feature vector set formed by arranging the feature values obtained by C solutions in descending order; omega shape k Projection of intonation subspace P, J, formed for U k For original intonation sample I k Based on projection omega k And the estimated value obtained by the feature vector set U, the value of the intonation extracted from the voice with the emotion state alpha by the N-dimensional vector in the original intonation space O is equal to omega α Is I α Projection of the constructed subspace P;representing the projection vector of the kth intonation of emotion category α in space P.
7. The teaching language specification level analysis method according to claim 1, wherein the calculation formula of the class question ratio is as follows:
wherein RoQ is the class question ratio, q is the sentence of the teacher in the teaching language, and NUM is the total sentence number of the teacher in the teaching language.
8. The teaching language specification level analysis system is characterized by comprising:
the audio alignment unit is used for acquiring audio materials through audio acquisition and aligning the voice volume and the voice tone in the audio materials according to the time base line of the audio materials;
the voice conversion unit is used for carrying out text conversion on the aligned audio materials to obtain text materials and classifying teaching language basic data in the text materials;
the parameter calculation unit is used for determining a voice index parameter corresponding to the classification result, wherein the voice index parameter comprises at least one of the following components: spoken Buddhist frequency, volume rationality, speech speed rationality, intonation rationality, word distribution characteristics, sentence emotion polarity, and class question rate;
and the analysis processing unit is used for outputting the result of the teaching language specification level analysis according to the voice index parameters.
9. Teaching language specification level analysis device, characterized in that the device comprises:
at least one processor;
at least one memory for storing at least one program;
the at least one program, when executed by the at least one processor, causes the at least one processor to perform the teaching language specification level analysis method of any one of claims 1-7.
10. A storage medium having stored therein a processor-executable program which, when executed by a processor, is adapted to run the teaching language specification level analysis method of any one of claims 1-7.
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