CN110992754B - High-efficiency pre-examination, self-learning and teaching method for oral English - Google Patents
High-efficiency pre-examination, self-learning and teaching method for oral English Download PDFInfo
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- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
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Abstract
The invention provides a high-efficiency oral English preparation, self-study and teaching method, which comprises the following steps: acquiring a first answer of a user to a preset spoken question in spoken English; performing vocabulary supplementation processing based on the first answer to obtain a second answer; and performing vocabulary fission processing based on the second answer to obtain a final answer, and supplementing and fragmenting the answer of the user to improve the learning efficiency of the user.
Description
Technical Field
The invention relates to the technical field of oral English, in particular to an efficient pre-examination, self-study and teaching method for oral English.
Background
In the current teaching of English and other various languages in China, including training teaching of some foreign examinations such as Abutofu, the oral teaching is a short board of each large education institution, and the most common main teaching mode is to forcibly and directly teach students to remember some verbose and useless templates or to invisibly guide students to accept templates. Therefore, the practical spoken language communication is very important and unclear in thought, and the method is a pain point for wide English spoken language clients and students. In the examination of Yasi and the like, the answers of the template are pressed and scored by the examiner, so that the student spends a large amount of charges and still cannot obtain the ideal score. Therefore, it is important to provide a method suitable for learning various spoken languages, including the application of a national english spoken language examination such as yasi tofu.
Disclosure of Invention
The invention provides a high-efficiency preparation, self-study and teaching method for spoken English, which is used for improving the learning efficiency of a user by supplementing and splitting answers of the user.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English, which comprises the following steps:
acquiring a first answer of a user to a preset spoken question in spoken English;
performing vocabulary supplementation processing based on the first answer to obtain a second answer;
and performing vocabulary fission treatment based on the second answer to obtain a final answer.
In one possible way of realisation,
the specific step of obtaining the first answer of the user to the preset spoken language question in the spoken English language comprises the following steps:
acquiring user information of the user;
and determining a first answer of the preset spoken language question according to the user information.
In one possible way of realisation,
the specific steps of performing vocabulary supplementation processing based on the first answer to obtain a second answer include:
performing vocabulary splitting processing on all vocabularies in the first answer, and determining preset vocabularies after the vocabulary splitting processing;
determining high-frequency vocabularies in the preset vocabularies based on a vocabulary database;
and expanding the supplementary vocabulary related to the determined high-frequency vocabulary, and outputting the supplementary vocabulary based on the first answer to obtain a second answer.
In one possible way of realisation,
the specific step of performing vocabulary fission processing based on the second answer to obtain a final answer includes:
acquiring the supplementary vocabulary in the second answer;
outputting a prompt vocabulary related to the supplementary vocabulary;
and determining fission vocabularies related to the supplementary vocabularies based on the prompt vocabularies, and outputting the fission vocabularies based on the second answers to obtain final answers.
In one possible way of realisation,
prior to performing the vocabulary fission process based on the second answer, further comprising:
determining a spoken language objective of the user;
determining the training level of the user according to the determined spoken language target;
the training level is associated with a predetermined number of supplements of supplementary vocabulary and a predetermined number of fissions of fissile vocabulary.
In one possible way of realisation,
before the obtaining of the final answer, the method further comprises:
acquiring a preset spoken language problem in the spoken English;
cutting the acquired preset spoken language problem to obtain a preset number of problem blocks;
determining the topicality of each question block based on a pre-stored topicality database;
guiding the user to answer the corresponding question block according to the determined topicality;
and determining the degree of association among the question blocks, determining a final answer obtained by the user according to the degree of association result, the answer result and the vocabulary fission processing result, and outputting the final answer.
In one possible way of realisation,
after obtaining the final answer, the method further comprises:
collecting voice data of a final answer of the user;
performing blank processing on the collected voice data, and obtaining blank frames in voice sections corresponding to the voice data;
deleting the blank frame and reserving the rest frames in the voice section;
acquiring the voice texts of the remaining frames, determining whether each text vocabulary in the voice texts is complete, and if so, sending a first alarm instruction;
otherwise, extracting suspected defect words in the voice text, and labeling the suspected defect words;
determining the attribute characteristics of the marked suspected defect vocabulary, determining the current position of the current suspected defect vocabulary according to the determined attribute characteristics, and dividing the position area of the current position of the current suspected defect vocabulary based on a first preset interval and a second preset interval;
determining confidence values of the current suspected defect vocabulary and regional vocabularies in the position regions after division processing, and judging that the current suspected defect vocabulary is a correct vocabulary when the confidence values are larger than preset values;
when the confidence value is not larger than the preset value, judging that the current suspected defect vocabulary is an error vocabulary, and meanwhile, acquiring the vocabulary intention of each regional vocabulary in the position region;
determining the regional intention of the position region according to the acquired vocabulary intention based on a pre-stored user intention database;
according to the regional intention, correcting the wrong vocabulary, judging the confidence values of the corrected wrong vocabulary and regional vocabulary in the position region, when the confidence values are larger than a preset value, judging that the corrected wrong vocabulary is a correct vocabulary, and updating the current suspected defect vocabulary into the correct vocabulary;
meanwhile, when all the marked suspected defect vocabularies are updated to correct vocabularies, a second alarm instruction is sent;
otherwise, when the corrected error vocabulary is judged to be the error vocabulary, the correction processing of the error vocabulary is repeatedly executed until the confidence value is larger than the preset value.
In one possible way of realisation,
in the process of sending the first alarm indication, the method further comprises the following steps:
determining a voice value corresponding to each text vocabulary, and performing superposition processing on each determined voice value to obtain a total voice value;
and compressing the voice total value obtained after the superposition processing, and transmitting the voice data subjected to the compression processing to a user side for displaying.
In one possible implementation of the method according to the invention,
in the process of acquiring the first answer of the user to the preset spoken question in the spoken English, the method further comprises the following steps:
step A1, acquiring answers of the user to preset spoken question questions in spoken English, determining the answers of the questions as recognition answers, and determining the short-time energy of the recognition answers according to a formula (1);
wherein Pf is the short-time energyAmount, KiThe voice size of the i frame voice of the recognition answer, P is a preset energy coefficient, N is the frame length of the recognition answer, aiThe frequency of the ith frame of the identification answer is X, the total duration of the identification answer is 1, 2 and 3 … N;
step A2, determining a window coefficient of the recognition answer according to formula (2);
where Df is the window coefficient, ZlThe speech pitch of the frame I of the recognition answer is T, T is a preset standard pitch value, maxZ is the maximum value of the speech pitch of the recognition answer, y is a multiplied parameter, and l is 1, 2 and 3 … N;
step A3, determining a recognition audio frequency according to formula (3) according to the short-time energy of the recognition answer determined in the step A1 and the window coefficient of the recognition answer determined in the step A2;
wherein Pg is the identification audio frequency, ZiA speech pitch of an i-th frame of the recognition answer;
and A4, performing voice recognition on the part of the recognition answers with the audio frequency greater than the recognition audio frequency to obtain a first recognized answer, and performing vocabulary supplementation processing on the first recognized answer to obtain a second answer.
In one possible way of realisation,
acquiring a first language of the user based on a preset spoken language problem in spoken English;
determining target output of the user based on a preset vocabulary comparison database and a first language;
the preset vocabulary comparison database is obtained by recording the text vocabulary input by the user and carrying out preset labeling on the text vocabulary;
in the process of determining the target output of the user, the method further comprises:
when the user answers a preset spoken question in the spoken English, acquiring a candidate word related to the first language, forming the candidate word into a sentence to be contrasted, and acquiring a target sentence related to the sentence to be contrasted based on a preset word contrast database;
when a query word exists in the candidate words, determining a characteristic statement of the query word according to a preset word characteristic database;
after determining the target output of the user, further comprising:
and acquiring a candidate phrase output by the target, performing preset processing on the candidate phrase on the basis of the target output, and acquiring a second language.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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. In the drawings:
FIG. 1 is a flow chart of an efficient examination preparation, self-study and teaching method for spoken English in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English, which comprises the following steps of:
step 1: acquiring a first answer of a user to a preset spoken question in spoken English;
step 2: performing vocabulary supplementation processing based on the first answer to obtain a second answer;
and step 3: and performing vocabulary fission treatment based on the second answer to obtain a final answer.
The preset spoken language question is preset;
examples of the first answer, the second answer, and the final answer are as follows:
when the preset spoken language question is: do you work or Do you study? (is you at work, or at school
The corresponding first answer is typically a direct answer to the question by the user, such as: now I'm students (in school), etc.;
on the basis of the first answer, vocabulary supplementation processing is performed, and the obtained second answer can be: well, I'm student right now, I'm an undersgrate student at the City University of Moscow (I am in school, I am a president of the University of the Moscow City);
performing vocabulary fission processing on the basis of the second answer, wherein the obtained final answer can be: well, I'm study right now, I'm an understandate student at the City University of Moscow, during my students, I'm teaching somethylated calamitics and human anguages, and now this is my first year as a fresh man (I am in school, I am a preschool of Moscow City University, in school period, doing some human linguistic things, now the first year of I am), and the like.
The vocabulary supplementing process may be vocabulary supplementing the learning (students) in the first answer, such as the study calendar (undersgrarate) related to the learning (students) and the name of the school in which the learning is located (the City University of Moscow);
wherein the vocabulary fission process may be a further detailed explanation or description of the academic calendar (undersgrarate) in the second answer, or a concurrent occurrence, or one or more of the possible cases;
such as fission with logical cues: the method comprises the following steps: a time-like language during school (training my students) elicits an explanation of what professions were learned in school (sensing called linear and human languages) and the current grade (first year as a freshman).
Through the supplement and the fission, the learning efficiency of the user can be effectively improved, and the low learning efficiency of the user caused by the fixed template format is avoided.
The beneficial effects of the above technical scheme are: by supplementing and splitting the answers of the user, the learning efficiency of the user is improved.
The embodiment of the invention provides a high-efficiency preparation, self-study and teaching method for spoken English, wherein the specific steps of acquiring a first answer of a user to a preset spoken question in spoken English comprise the following steps:
acquiring user information of the user;
and determining a first answer of the preset spoken language question according to the user information.
The user information may be, for example, a current occupation of the user.
The beneficial effects of the above technical scheme are: by acquiring the user information, the accuracy of the first answer is improved conveniently.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English,
the specific steps of performing vocabulary supplementation processing based on the first answer to obtain a second answer include:
performing vocabulary splitting processing on all vocabularies in the first answer, and determining preset vocabularies after the vocabulary splitting processing;
determining high-frequency vocabularies in the preset vocabularies based on a vocabulary database;
and expanding the supplementary vocabulary related to the determined high-frequency vocabulary, and outputting the supplementary vocabulary based on the first answer to obtain a second answer.
Performing vocabulary splitting processing on all vocabularies in the first answer, and determining preset vocabularies after the vocabulary splitting processing, such as: the preset vocabulary in Now I' mstudying (in school) includes: now, I, am and student, wherein the determined high-frequency vocabulary is student, the student is extended to obtain the academic calendar (underscourate) related to student and the name of the school in which student is located, and then the second answer is obtained.
The beneficial effects of the above technical scheme are: through vocabulary supplementary processing, the richness of vocabulary and the flexibility of grammar structure are improved, and the accuracy of obtaining the second answer is improved conveniently.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English,
the specific step of performing vocabulary fission processing based on the second answer to obtain a final answer includes:
acquiring the supplementary vocabulary in the second answer;
outputting a prompt vocabulary related to the supplementary vocabulary;
and determining fission vocabularies related to the supplementary vocabularies based on the prompt vocabularies, and outputting the fission vocabularies based on the second answers to obtain final answers.
The supplementary words are, for example: the school calendar (undersgrarate) and the name of the school in which it is learned (the City University of Moscow);
the corresponding prompt vocabulary is, for example: logical vocabularies, and including, but not limited to, temporal or local vocabularies, may be usually (in general), sometimes (in some cases), or anything such as on the weekdays, during the holidays, etc.; but can also be But; parallel points such as first, second, third; or may be a simultaneous occurrence, with a idiom, such as doing/done;
where, if multiple possible situations are to be given, for example, an or (or) may be used as the lead-out logical vocabulary.
The fission vocabulary may be any one or more of the prompt vocabulary.
The beneficial effects of the above technical scheme are: through the vocabulary fission, the richness of the vocabulary and the flexibility of a grammar structure are further improved, and the flexibility and the accuracy of acquiring a third answer by a astronaut are facilitated.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English,
prior to performing the vocabulary fission process based on the second answer, further comprising:
determining a spoken language objective of the user;
determining the training level of the user according to the determined spoken language target;
the training level is associated with a predetermined number of supplements of supplementary vocabulary and a predetermined number of fissions of fissile vocabulary.
The above spoken language target, taking yasi spoken language as an example, corresponds to the spoken language target: the target score is 6.0 points, 6.5 points, 7.0 points and the like;
the training grades correspond to the target scores;
when the target score is 6.0 or 6.5 points, the corresponding training level may be: amplifying two supplementary words and two fission words in the first answer;
when the target score is 7.0 minutes, the corresponding training level may be: three supplementary words and three fission words are augmented in the first answer.
The beneficial effects of the above technical scheme are: through the spoken language target, the training grade suitable for the user is convenient to determine, and the method has pertinence and is convenient to improve the learning efficiency of the user.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English,
before the obtaining of the final answer, the method further comprises:
acquiring a preset spoken language problem in the spoken English;
cutting the acquired preset spoken language problem to obtain a preset number of problem blocks;
determining the topicality of each question block based on a pre-stored topicality database;
guiding the user to answer the corresponding question block according to the determined topicality;
and determining the degree of association among the question blocks, determining a final answer obtained by the user according to the degree of association result, the answer result and the vocabulary fission processing result, and outputting the final answer.
The cutting process is performed to obtain a preset number of question blocks, wherein the preset number is determined according to the number of questions, and each question block may contain one question;
the topic degree of the problem block can be, for example, the topic degree of learning, work and the like;
the above-mentioned determining the degree of association between the question blocks, for example, determining the degree of association between learning and work, is convenient for improving the reliability of obtaining work answers, and the like.
The beneficial effects of the above technical scheme are: the accuracy of the final answer is improved conveniently.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English,
after obtaining the final answer, the method further comprises:
collecting voice data of a final answer of the user;
performing blank processing on the collected voice data, and obtaining blank frames in voice sections corresponding to the voice data;
deleting the blank frame and reserving the rest frames in the voice section;
acquiring the voice texts of the remaining frames, determining whether each text vocabulary in the voice texts is complete, and if so, sending a first alarm instruction;
otherwise, extracting suspected defect words in the voice text, and labeling the suspected defect words;
determining the attribute characteristics of the marked suspected defect vocabulary, determining the current position of the current suspected defect vocabulary according to the determined attribute characteristics, and dividing the position area of the current position of the current suspected defect vocabulary based on a first preset interval and a second preset interval;
determining confidence values of the current suspected defect vocabulary and regional vocabularies in the position regions after division processing, and judging that the current suspected defect vocabulary is a correct vocabulary when the confidence values are larger than preset values;
when the confidence value is not larger than the preset value, judging that the current suspected defect vocabulary is an error vocabulary, and meanwhile, acquiring the vocabulary intention of each regional vocabulary in the position region;
determining the regional intention of the position region according to the acquired vocabulary intention based on a pre-stored user intention database;
according to the regional intention, correcting the wrong vocabulary, judging the confidence values of the corrected wrong vocabulary and regional vocabulary in the position region, when the confidence values are larger than a preset value, judging that the corrected wrong vocabulary is a correct vocabulary, and updating the current suspected defect vocabulary into the correct vocabulary;
meanwhile, when all the marked suspected defect vocabularies are updated to correct vocabularies, a second alarm instruction is sent;
otherwise, when the corrected error vocabulary is judged to be the error vocabulary, the correction processing of the error vocabulary is repeatedly executed until the confidence value is larger than the preset value.
The blank processing is carried out on the voice data, so that blank frames in the voice data are removed, the occupation of storage space is reduced, and the subsequent processing efficiency of the voice data is improved;
and the blank frame does not contain any voice information;
the voice text is obtained by performing voice-character conversion according to the voice data with blank frames removed;
the first alarm indication may be that the text vocabulary is complete;
the completeness of the text vocabulary is judged, so that data loss is avoided;
labeling the suspected defect vocabulary in the voice text, for example: the vocabulary of suspected defects is: eeemmm, therefore, the eeemmm can be marked in highlighting marking and other marking modes;
the attribute characteristics of the suspected defect vocabulary are as follows: properties such as nouns, verbs, adjectives, adverbs, and the like;
such as: if the suspected defective vocabulary is the verb, the intermediate position of the sentence corresponding to the verb can be conveniently determined;
the above-mentioned dividing process is performed on the position area of the current position of the current suspected-of-defect vocabulary based on the first preset interval and the second preset interval, for example, the first preset interval includes a position corresponding to a noun, and the second preset interval includes a position corresponding to an object, so that the dividing process results in: noun + verb + object;
the confidence value is used for determining a noun + verb + object, and in the sentence, the association degree of the noun and the verb and the association degree of the verb and the object;
the preset value can be 95% or more;
when the confidence value is not larger than the preset value, judging that the current suspected defect is an error vocabulary, and meanwhile, acquiring the vocabulary intention of each area vocabulary in the position area;
the above-mentioned judgment of the suspected defect vocabulary is to ensure the correctness of the suspected defect vocabulary.
The word intention is to obtain a noun intention, a verb intention and an object intention in a sentence consisting of a noun, a verb and an object so as to obtain a regional topic map of the position region;
the second warning indication may be, for example, a text vocabulary correct warning indication;
the beneficial effects of the above technical scheme are: the blank frames are removed, the data processing time is saved, the processing efficiency is improved, the data loss is avoided by judging the integrity of text vocabularies, the suspected defective vocabularies are judged correctly and wrongly to reduce the error rate in the labeling process, the suspected defective vocabularies are corrected to improve the accuracy of data acquisition, and the warning indication is performed to remind a user conveniently.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English,
in the process of sending the first alarm indication, the method further comprises the following steps:
determining a voice value corresponding to each text vocabulary, and performing superposition processing on each determined voice value to obtain a total voice value;
and compressing the voice total value obtained after the superposition processing, and transmitting the voice data subjected to the compression processing to a user side for displaying.
The speech value may be a speech energy value in a speech frame at the text vocabulary corresponding to each text vocabulary;
the superposition processing is carried out to obtain a total voice value, so that the storage space corresponding to the voice data and the corresponding vocabulary quantity are conveniently determined, and the final answer of the user is conveniently judged in a form, wherein the form refers to the storage space and the vocabulary quantity;
the complexity of the content of the text information can be judged through the storage space and the number of the vocabularies;
the user side can be an electronic device such as a smart phone and a notebook computer;
the compression process is performed to reduce the storage capacity and improve the transmission efficiency.
The beneficial effects of the above technical scheme are: by compressing, the storage capacity is reduced, the transmission efficiency is improved, and the user can watch the information in time.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English,
in the process of acquiring the first answer of the user to the preset spoken question in the spoken English, the method further comprises the following steps:
step A1, acquiring answers of the user to preset spoken question questions in spoken English, determining the answers of the questions as recognition answers, and determining the short-time energy of the recognition answers according to a formula (1);
wherein Pf is the short-term energy, KiThe voice size of the i frame voice of the recognition answer, P is a preset energy coefficient, N is the frame length of the recognition answer, aiThe frequency of the ith frame of the identification answer is X, the total duration of the identification answer is 1, 2 and 3 … N;
and P is generally preset to 0.63;
step A2, determining a window coefficient of the recognition answer according to formula (2);
where Df is the window coefficient, ZlThe speech pitch of the frame I of the recognition answer is T, T is a preset standard pitch value, maxZ is the maximum value of the speech pitch of the recognition answer, y is a multiplied parameter, and l is 1, 2 and 3 … N;
step A3, determining a recognition audio frequency according to formula (3) according to the short-time energy of the recognition answer determined in the step A1 and the window coefficient of the recognition answer determined in the step A2;
wherein Pg is the identification audio frequency, ZiA speech pitch of an i-th frame of the recognition answer;
and, T is generally preset to 690;
and A4, performing voice recognition on the part of the recognition answers with the audio frequency greater than the recognition audio frequency to obtain a first recognized answer, and performing vocabulary supplementation processing on the first recognized answer to obtain a second answer.
Has the advantages that: by means of the technology, in the process of identifying the first answer of the preset spoken language question in the spoken English of the user, through intelligent judgment, the part, containing effective voice, of the first answer can be determined, accordingly, redundant information of the first answer is reduced, the efficiency of voice identification is improved, meanwhile, by means of the technology, the calculation amount in the voice identification process can be reduced, the influence of the environment on the voice identification is reduced, and the anti-noise capacity is improved.
The embodiment of the invention provides a high-efficiency examination preparation, self-study and teaching method for spoken English, which further comprises the following steps:
acquiring a first language of the user based on a preset spoken language problem in spoken English;
determining target output of the user based on a preset vocabulary comparison database and a first language;
the preset vocabulary comparison database is obtained by recording the text vocabulary input by the user and carrying out preset labeling on the text vocabulary;
in the process of determining the target output of the user, the method further comprises:
when the user answers a preset spoken question in the spoken English, acquiring a candidate word related to the first language, forming the candidate word into a sentence to be contrasted, and acquiring a target sentence related to the sentence to be contrasted based on a preset word contrast database;
when a query word exists in the candidate words, determining a characteristic statement of the query word according to a preset word characteristic database;
after determining the target output of the user, further comprising:
and acquiring a candidate phrase output by the target, performing preset processing on the candidate phrase on the basis of the target output, and acquiring a second language.
For example, in the process of a jazz test, the first language of the user is obtained, namely, the sentence composed of the Chinese language;
the vocabulary comparison database is a Chinese-English vocabulary comparison database;
the corresponding target output is English sentences which are contrasted with sentences composed of Chinese;
the preset labeling is performed on the text vocabulary, for example, semantic labeling is performed on english vocabulary;
obtaining candidate vocabularies related to the first language, wherein the candidate vocabularies may be noun vocabularies or verb vocabularies in the first language;
the candidate vocabularies form sentences to be contrasted, and target sentences relevant to the sentences to be contrasted are obtained based on a preset vocabulary contrast database;
the candidate vocabulary can be: the words of structure, preposition phrase words and words corresponding to the fixed language clauses;
when the term of structure is used, for example: when the sentence to be contrasted is "i like the color of the table"; the corresponding target statement is "I like the color of this table", wherein "the" may be a candidate vocabulary, and the vocabulary compared with "the" is "of";
when a preposition phrase vocabulary, for example: "on the table" may be contrasted with "on the table";
"from Australia" is contrasted with "from Australia";
when the words correspond to the fixed phrase clauses, for example: "I am the time" control is "the time we I was you young"; as another example, "the color of I'm tables in Australia" contrasts with "the color of the table at I bone from Australia" and the like;
the query words can be words unfamiliar to the user, and the query words can be nouns, verbs or modifiers;
when the query word is a noun, determining the characteristic sentence of the noun according to a preset word characteristic database, such as: the determination is made based on the characteristic database of the definite language clauses + simple words,
for example, when the noun is: when a vacuum cleaner (vacuum cleaner), based on a characteristic database of a definite clause and a simple vocabulary, the determined noun characteristic sentence is as follows: a kind of machine that cleans the floor;
for another example, when the noun is a rainbow (rainbow), the noun feature sentence is determined to be: the driving that uses all hands in the skin after the rain, it has a Man in the hair color like red, green or purple (the things that hang in the air after rain, there are many beautiful colors, such as red, green or purple), etc.;
when the query word is a verb, determining a characteristic statement of the verb according to a preset lexical characteristic database, for example, when the lexical characteristic database is a universal verb, such as take, have, get, make or do, the determined characteristic statement of the verb is, for example: fry make a food, make an appointment, etc.
When the query word is a modifier, determining the characteristic sentence of the modifier according to a preset word characteristic database, wherein the modifiers are divided into two types, namely, the adjective of the modified noun and the adverb of the modified verb or the degree of the adjective.
For example, for positive adjectives, simple good, nice, perfect, wonderful, brilliant, great, awesome, very effective;
directly using very well, very 50 and very effect for positive adverbs;
directly using not good, bad for negative adjectives;
directly using a not so well, a not very well and the like for negative adverbs;
the method has the advantage that the fluency of the target sentence is improved conveniently by determining the characteristic sentences of the query words.
The candidate phrases in the above-mentioned acquisition target output are, for example, noun phrases or verb phrases acquired;
the preset processing is performed to modify the candidate phrase, for example: adding a modified vocabulary on the basis of a noun or a noun phrase, wherein the modified vocabulary is divided into a front modified vocabulary and a rear modified vocabulary;
wherein, the preposed modification vocabulary can be adjectives or adjective phrases;
the post-positioned modification words can be definite language clauses or prepositional phrases;
for example, the following description obtains a candidate phrase output by a target, performs preset processing on the candidate phrase on the basis of the target output, and obtains a second language, for example:
the target output is: i went to a party, which has a simple structure and a small amount of information.
The candidate phrases thus obtained are: party, and modifying the candidate phrase to obtain a second language: i went to a top removable sponge wald by my friends from Mexico (I participated in a very memorable Spanish party gathering, held by my Mexico friends);
and for complex verb phrases, only modified adverb or adverb phrases need to be added before or after the verb, wherein the adverb or adverb phrase may be a way to do a thing, degree, which shows the speaker's idea or evaluation of a certain thing, or expression of emotion and thought, for example:
the target output is: he is interested because He told me bathing (He is interesting because He tells me something);
after the modification treatment, the obtained second language is: he is intersection of a because He top effect of a bottom of a sensing with a just a now pieces of paper and a pencil (He is very interesting because He only uses a few pieces of paper and a pencil to effectively tell me something), etc.
The beneficial effects of the above technical scheme are: the comparison of language vocabularies is convenient for improve the learning efficiency, the recording of text vocabularies is convenient for increase the text capacity of the vocabularies, the determination of the characteristics of the doubtful vocabularies is convenient for improve the fluency returned by the doubtful vocabularies, the preset processing is carried out on the candidate phrases, and the complexity of the target output of the candidate phrases is convenient for improve.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. An efficient oral English preparation, self-study and teaching method is characterized by comprising the following steps:
acquiring a first answer of a user to a preset spoken question in spoken English;
performing vocabulary supplementation processing based on the first answer to obtain a second answer;
performing vocabulary fission treatment based on the second answer to obtain a final answer;
before the obtaining of the final answer, the method further comprises:
acquiring a preset spoken language problem in the spoken English;
cutting the acquired preset spoken language problem to obtain a preset number of problem blocks;
determining the topicality of each question block based on a pre-stored topicality database;
guiding the user to answer the corresponding question block according to the determined topicality;
and determining the degree of association among the question blocks, determining a final answer obtained by the user according to the degree of association result, the answer result and the vocabulary fission processing result, and outputting the final answer.
2. The method of claim 1, wherein the step of obtaining a first answer to a predetermined spoken question from a user in spoken english comprises:
acquiring user information of the user;
and determining a first answer of the preset spoken language question according to the user information.
3. The method of claim 1, wherein the step of performing vocabulary supplementation based on the first answer to obtain a second answer comprises:
performing vocabulary splitting processing on all vocabularies in the first answer, and determining preset vocabularies after the vocabulary splitting processing;
determining high-frequency vocabularies in the preset vocabularies based on a vocabulary database;
and expanding the supplementary vocabulary related to the determined high-frequency vocabulary, and outputting the supplementary vocabulary based on the first answer to obtain a second answer.
4. The method of claim 3, wherein the step of performing a vocabulary fission process based on the second answer to obtain a final answer comprises:
acquiring the supplementary vocabulary in the second answer;
outputting a prompt vocabulary related to the supplementary vocabulary;
and determining fission vocabularies related to the supplementary vocabularies based on the prompt vocabularies, and outputting the fission vocabularies based on the second answers to obtain final answers.
5. The method of claim 4, prior to performing the vocabulary fission process based on the second answer, further comprising:
determining a spoken language objective of the user;
determining the training level of the user according to the determined spoken language target;
the training level is associated with a predetermined number of supplements of supplementary vocabulary and a predetermined number of fissions of fissile vocabulary.
6. The method of claim 1, wherein after obtaining the final answer, the method further comprises:
collecting voice data of a final answer of the user;
performing blank processing on the collected voice data, and obtaining blank frames in voice sections corresponding to the voice data;
deleting the blank frame and reserving the rest frames in the voice section;
acquiring the voice texts of the remaining frames, determining whether each text vocabulary in the voice texts is complete, and if so, sending a first alarm instruction;
otherwise, extracting suspected defect words in the voice text, and labeling the suspected defect words;
determining the attribute characteristics of the marked suspected defect vocabulary, determining the current position of the current suspected defect vocabulary according to the determined attribute characteristics, and dividing the position area of the current position of the current suspected defect vocabulary based on a first preset interval and a second preset interval;
determining confidence values of the current suspected defect vocabulary and regional vocabularies in the position regions after division processing, and judging that the current suspected defect vocabulary is a correct vocabulary when the confidence values are larger than preset values;
when the confidence value is not larger than the preset value, judging that the current suspected defect vocabulary is an error vocabulary, and meanwhile, acquiring the vocabulary intention of each regional vocabulary in the position region;
determining the regional intention of the position region according to the acquired vocabulary intention based on a pre-stored user intention database;
according to the regional intention, correcting the wrong vocabulary, judging the confidence values of the corrected wrong vocabulary and regional vocabulary in the position region, when the confidence values are larger than a preset value, judging that the corrected wrong vocabulary is a correct vocabulary, and updating the current suspected defect vocabulary into the correct vocabulary;
meanwhile, when all the marked suspected defect vocabularies are updated to correct vocabularies, a second alarm instruction is sent;
otherwise, when the corrected error vocabulary is judged to be the error vocabulary, the correction processing of the error vocabulary is repeatedly executed until the confidence value is larger than the preset value.
7. The method of claim 6, wherein in transmitting the first alarm indication, further comprising:
determining a voice value corresponding to each text vocabulary, and performing superposition processing on each determined voice value to obtain a total voice value;
and compressing the voice total value obtained after the superposition processing, and transmitting the voice data subjected to the compression processing to a user side for displaying.
8. The method of claim 1, wherein in obtaining a first answer to a predetermined spoken question by a user in spoken english, further comprising the steps of:
step A1, acquiring answers of the user to preset spoken question questions in spoken English, determining the answers of the questions as recognition answers, and determining the short-time energy of the recognition answers according to a formula (1);
wherein the content of the first and second substances,
for the purpose of the short-time energy,
for the identificationFirst of the answer
The speech size of the frame speech is,
in order to pre-set the energy factor,
for the frame length of the recognition answer,
is the first of the recognition answers
The frequency of the frame is determined by the frequency of the frame,
for the total duration of the recognition answer,
；
step A2, determining a window coefficient of the recognition answer according to formula (2);
wherein the content of the first and second substances,
for the said window coefficient(s) it is,
is the first of the recognition answers
The pitch of the speech of the frame,
in order to preset the standard pitch value,
is the maximum value of the voice pitch of the recognition answer,
in order to be the parameters to be integrated,
；
step A3, determining a recognition audio frequency according to formula (3) according to the short-time energy of the recognition answer determined in the step A1 and the window coefficient of the recognition answer determined in the step A2;
wherein the content of the first and second substances,
in order for the audio frequency to be identified,
is the first of the recognition answers
The speech pitch of the frame;
and A4, performing voice recognition on the part of the recognition answers with the audio frequency greater than the recognition audio frequency to obtain a first recognized answer, and performing vocabulary supplementation processing on the first recognized answer to obtain a second answer.
9. The method of claim 1, further comprising:
acquiring a first language of the user based on a preset spoken language problem in spoken English;
determining target output of the user based on a preset vocabulary comparison database and a first language;
the preset vocabulary comparison database is obtained by recording the text vocabulary input by the user and carrying out preset labeling on the text vocabulary;
in the process of determining the target output of the user, the method further comprises:
when the user answers a preset spoken question in the spoken English, acquiring a candidate word related to the first language, forming the candidate word into a sentence to be contrasted, and acquiring a target sentence related to the sentence to be contrasted based on a preset word contrast database;
when a query word exists in the candidate words, determining a characteristic statement of the query word according to a preset word characteristic database;
after determining the target output of the user, further comprising:
and acquiring a candidate phrase output by the target, performing preset processing on the candidate phrase on the basis of the target output, and acquiring a second language.
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| CN106991094A (en) * | 2016-01-21 | 2017-07-28 | 何钰威 | Foreigh-language oral-speech is spoken learning system, method and computer program |
| CN107133303A (en) * | 2017-04-28 | 2017-09-05 | 百度在线网络技术（北京）有限公司 | Method and apparatus for output information |
| CN108596356A (en) * | 2018-01-25 | 2018-09-28 | 河南工学院 | English Score prediction technique based on big data and the server for English Score prediction |
| CN109493658A (en) * | 2019-01-08 | 2019-03-19 | 上海健坤教育科技有限公司 | Situated human-computer dialogue formula spoken language interactive learning method |
| CN110428853A (en) * | 2019-08-30 | 2019-11-08 | 北京太极华保科技股份有限公司 | Voice activity detection method, Voice activity detection device and electronic equipment |
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| CN106991094A (en) * | 2016-01-21 | 2017-07-28 | 何钰威 | Foreigh-language oral-speech is spoken learning system, method and computer program |
| CN107133303A (en) * | 2017-04-28 | 2017-09-05 | 百度在线网络技术（北京）有限公司 | Method and apparatus for output information |
| CN108596356A (en) * | 2018-01-25 | 2018-09-28 | 河南工学院 | English Score prediction technique based on big data and the server for English Score prediction |
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