CN101739867A - Method for scoring interpretation quality by using computer - Google Patents
Method for scoring interpretation quality by using computer Download PDFInfo
- Publication number
- CN101739867A CN101739867A CN200810226673A CN200810226673A CN101739867A CN 101739867 A CN101739867 A CN 101739867A CN 200810226673 A CN200810226673 A CN 200810226673A CN 200810226673 A CN200810226673 A CN 200810226673A CN 101739867 A CN101739867 A CN 101739867A
- Authority
- CN
- China
- Prior art keywords
- model
- scoring
- translation
- recognition result
- interpreter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000013519 translation Methods 0.000 claims abstract description 37
- 238000012549 training Methods 0.000 claims abstract description 22
- 238000005516 engineering process Methods 0.000 claims abstract description 16
- 230000007246 mechanism Effects 0.000 claims abstract description 4
- 230000008569 process Effects 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 claims description 2
- 238000004321 preservation Methods 0.000 claims description 2
- 230000004927 fusion Effects 0.000 claims 1
- 238000012821 model calculation Methods 0.000 claims 1
- 238000012545 processing Methods 0.000 abstract description 8
- 238000011156 evaluation Methods 0.000 abstract description 7
- 230000000694 effects Effects 0.000 abstract description 3
- 238000012790 confirmation Methods 0.000 abstract 1
- 238000004422 calculation algorithm Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 239000000654 additive Substances 0.000 description 2
- 230000000996 additive effect Effects 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 241000209202 Bromus secalinus Species 0.000 description 1
- 235000012364 Peperomia pellucida Nutrition 0.000 description 1
- 240000007711 Peperomia pellucida Species 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000001915 proofreading effect Effects 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 230000001755 vocal effect Effects 0.000 description 1
Images
Abstract
The invention discloses a method for scoring interpretation quality by using a computer, which comprehensively uses computer voice identification, voice pronunciation estimation, text translation quality confirmation technology to obtain the interpretation quality of a testee. The method comprises the following steps: establishing a database aiming at the characteristics of the tested pronunciation crowd; training the database by using a large vocabulary continuous speech acoustic model training platform to obtain an acoustic model; collecting corresponding expert knowledge and translated text linguistic data for each translation question to form a language model, a scoring model and a standard adjustment model required for identification; and finally, integrating the output result of a speech identifier and a linguistic processing mechanism to output the score of the interpretation quality of the testee, and providing feedback suggestion. The effect of machine evaluation already basically reaches the level close to expert evaluation; and meanwhile, the method also can provide some suggestions for testee pronunciation, vocabulary use and sentence pattern use during evaluating to guide the testee to correct.
Description
Technical field
The present invention proposes a kind of method of using computing machine that spoken translation quality is marked, belong to speech recognition, voice signal processing, computer-assisted language learning field.Be meant that specifically the utilization computing machine records to the personnel to be tested of Interpreter's topic type,, obtain the scoring and the feedback opinion of Interpreter's quality then by Computer Processing.
Background technology
The informationization of social life and economic globalization become increasingly conspicuous the importance of English.English has become most popular language in human lives's every field as one of most important information carrier.Many countries put in outstanding status all the important component part of English education as citizens'quality education, and with it in the basic education development strategy.
At present, the maximum verbal learning form of domestic employing is aspectant classroom instruction, because English teacher's shortage, the student is difficult to obtain man-to-man English study environment.In the face of this situation, the how tame unit in home and abroad has developed to use a computer and has carried out the system of Oral English Practice pronunciation diagnosis, these systems generally judge in short whether say, whether pronounce fluent, whether pronounce correct, but these technical ability are more elementary, are difficult to satisfy the more and more higher requirement of English learner.Because open topic type more can be understood tested personnel's thought process and language ability than the objective item of highly structural, in corresponding decision process, also can provide some more Useful Informations, and the also less influence that is subjected to exam-oriented education, cheats at one's exam of open topic type, in English study and examination, have very positive meaning.In recent years, used a computer and carry out text composition and appraise and come into vogue, but also not have to occur system that spoken language is write a composition and marked.For this reason, the present invention is directed to a kind of topic type commonly used in the study of SET and living English, Interpreter's topic type, carry out the computing machine scoring, this is compared with other software a very big raising, because except pronouncing fluently to the enunciator, pronounce accurately to judge, but also can the enunciator be passed judgment on, and can provide feedback opinion by vocabulary use, grammer use, whole translation situation.This is domestic or all is beyond example abroad.
Summary of the invention
Weak point at Oral English Practice examination and teaching the purpose of this invention is to provide the method that a kind of interactivity utilization computing machine good, that be not subjected to the time site limitation is marked to spoken translation quality.
For reaching described purpose, the invention provides the method that the utilization computing machine carries out the scoring of Oral English Practice translation quality, the present invention is made up of two parts:
The training part: training department divides and comprises the training acoustic model, language model, and scoring model and scoring feature are to the standard adjustment model of final scoring.
The scoring part: utilization digital signal processing theory and Computer Language Processing technology are marked to Interpreter's recording of tested people, make final scoring that machine provides as much as possible near expert's artificial scoring.
The present invention is achieved by the following technical solutions:
1) collection and foundation are at the database of tested personnel's group characteristic;
2) on Basis of Database, use big vocabulary continuous speech acoustic training model platform, obtain acoustic model;
3) each translation topic type is collected corresponding expertise and cypher text language material, in order to generate the language model that recognition system needs;
4) each translation topic type is collected corresponding corpus of text, form Interpreter's assessment data storehouse, generate the model that scoring needs then;
5) use the expert of the existing examination paper knowledge of giving a mark to carry out the adjustment of machine marking standard, obtain the standard adjustment model, be used to improve system performance;
6) voice to tested personnel carry out speech recognition, obtain recognition result;
7) from each model bank, obtain Interpreter's quality score feature with recognition result;
8) comprehensive all scoring features, by using the standard adjustment model, the voice that obtain tested personnel are finally marked, and provide feedback opinion.
Method of the present invention, can be used for using SET or Oral Training software Interpreter, picture talk, see speak spoken language scoring with similar topic type of video; Can apply to the right spoken quality scores of various language such as Chinese-English, English-Chinese, Chinese-French.
Beneficial effect of the present invention: judging the performance quality of computer examination, evaluating system, according to general in the world convention, is by judging that correlativity is judged between the artificial branch that scoring that computing machine provides and expert provide.Owing to also have certain error between the expert, so generally adopt average mark that 3-5 expert appraise as people's work point.
On test data, to process statistics on 7 experts' the artificial divided data storehouse, the correlativity minimum has 0.80 between the different experts, and maximum has 0.92.The scoring that the spoken translation quality points-scoring system of computing machine provides reaches 0.90 with the correlativity of expert's average mark, and therefore the machine effect of giving a mark has reached the approaching level of giving a mark with the expert substantially as can be seen.
The machine evaluation and test not only can substitute expert's scoring, can also provide some suggestions of tested personnel's pronunciation, vocabulary use, sentence pattern use in the evaluation and test process, instructs tested personnel to correct.
Description of drawings
Fig. 1 is the system chart of the methods of marking of the embodiment of the invention.
Fig. 2 is the process flow diagram of the embodiment of the invention.
Embodiment
Below in conjunction with the drawings and specific embodiments the present invention is done and to describe in further detail.
Terminological interpretation
Speech recognition: speech recognition technology is to allow machine by some relevant technology of artificial intelligence, voice signal is changed the text of journey correspondence or a technology of order, and the related field of speech recognition technology comprises: signal Processing, pattern-recognition, theory of probability and information theory, sound generating mechanism and hearing mechanism, artificial intelligence etc.
BLEU: the present the most widely used method for automatically evaluating in MT evaluation field is BLEU (Bilingual Evaluation Understudy), just based on the typical case's representative in these class methods of n unit coupling, is proposed in 2002 by IBM.Similarly method also comprises the NIST method, this method is proposed and name by Unite States Standard (USS) and technical institute, it is on the basis of BLEU method, taken all factors into consideration the weight of each n unit speech, composed to higher weight to embody the quantity of information that it is comprised for those occurrence number speech still less in reference translation.
The system chart of the methods of marking of the embodiment of the invention as shown in Figure 1, Interpreter's points-scoring system of realizing present embodiment runs on Microsoft's Window operating system, comprises Interpreter's expertise 1 Interpreter examinee's text 2, cypher text database 3, language model 4, scoring characteristic model 5, standard adjusting module 6, pronunciation extracting module 7, sound identification module 8, scoring characteristic extracting module 9, finally marks 10, acoustic model 11, pronunciation dictionary 12, feedback module 13 as a result.After tested personnel Interpreter voice are recorded, at first enter pronunciation extracting module 7 and extract the feature of dividing frame, this phonetic feature comprises energy and MFCC feature (the Mei Er cepstrum coefficient is promoted and the acoustic feature of derivation by people's auditory system achievement in research), and every frame is totally 39 dimensional features; Enter sound identification module 8 then,, select acoustic model 11, pronunciation dictionary 12, the language model 4 of use, utilize speech recognition engine that characteristic sequence is discerned according to tested personnel's sex and current Interpreter's topic type; The characteristic extracting module 9 of entering then to mark, this scoring characteristic extracting module 9 is extracted each feature of scoring characteristic models 5 correspondences: the model answer match condition of current recognition result, BLEU Model Matching situation, scoring characteristic model 5 match condition; Enter standard adjusting module 6 at last, scoring feature according to last module, use standard adjustment model 6 and parameter obtain tested personnel's the final scoring 10 of voice, and feedback module 13 comes out some suggestion feedbacks that tested personnel's pronunciation, vocabulary use, sentence pattern use as a result.Comprise that also acoustic model 11, language model 4 and the scoring characteristic model 5 that will obtain are saved in the system, each existing model of only need reloading that uses does not need training pattern again.
Complete flow process is made up of two parts:
The training part: training department divides and comprises training acoustic model 11, language model 4, scoring characteristic model 5 and the scoring feature standard adjustment model 6 to final scoring 10.
The scoring part: utilization digital signal processing theory and Computer Language Processing technology are marked to Interpreter's recording of tested people, and the final scoring 10 that the system that makes provides is as much as possible near expert's artificial scoring.
The present invention is achieved by the following technical solutions:
The method of Interpreter's quality score comprises a model training part, and training process may further comprise the steps:
(a) foundation is as follows at the speech database usage policy of tested personnel's group characteristic:
A.1 divide sex, seek a collection of agematched crowd according to the tested personnel crowd's of correspondence age distribution;
A.2 the principle according to the phoneme balance designs voice sample;
A.3 the designated person records according to the recording text, and voice are related with the foundation of corresponding text, and the voice document name is got identical filename with text, and different file suffixes can obtain its corresponding content of text from voice document rapidly like this;
(b) use continuous speech acoustic training model platform training to obtain acoustic model 11:
B.1 every training utterance is extracted 12 rank Mel cepstrums, normalized energy and constitute that totally 13 dimensions obtain 39 dimensional features by single order and second order difference then as essential characteristic;
B.2 by forcing alignment algorithm and front and back to be estimated, obtain the sub-acoustic model of single-tone to algorithm;
B.3 by design decision tree and front and back to algorithm, train to obtain the three-tone acoustic model;
B.4 by discrimination model training algorithm, train the three-tone acoustic model that obtains having discrimination information;
(c) use irrelevant N gram language model 4 interpolation of relevant N gram language model 4 of topic and topic to improve Interpreter's quality assessment performance.Each translation topic type is collected corresponding corpus of text, in order to generate the language model 4 that identification needs:
C.1 each translation topic type is collected corresponding corpus of text training and is obtained corresponding ternary language model 4, and corpus of text is from cypher text database 3;
C.2 in order to increase the identification compatibility of dialogue outlying content, adopt the training of common english text to obtain one three gram language model 4;
C.3 each translation topic type corresponding ternary language model 4 is by obtaining final three gram language model 4 of its appropriate translation topic type with general three gram language model, 4 interpolation;
(d) each translation topic type is collected corresponding corpus of text, forms Interpreter's assessment data storehouse, generate the characteristic model 5 that scoring needs then:
D.1 the foundation in Interpreter's model answer storehouse: each sense-group in the translation topic by a comprehensive N expert's cypher text, obtains the weight of model answer network He this network of current sense-group;
D.2 the foundation of Interpreter's model answer BLEU model bank: by a comprehensive N expert's cypher text, set up a BLEU model bank, cypher text is from cypher text database 3;
D.3 the foundation in Interpreter's text vector hierarchy model storehouse: Interpreter's expertise 1 of the cypher text by a comprehensive N expert and Interpreter's text 2 of the original examinee of each grade, set up the lexicon grammar vector storehouse of each grade, Interpreter's expertise 1 of expert's cypher text and examinee Interpreter text at different levels 2 are from cypher text database 3;
(e) standards of grading adjustment model 6 standards are adjusted the foundation in storehouse: use the expert of the existing examination paper knowledge of giving a mark to carry out the adjustment of machine marking standard, be used to improve system performance, every class Interpreter's topic type is collected the M road by examinee's examination question, provide scoring by L expert, and with the artificial scoring of expert's average mark as the per pass topic, introduce then based on forecast method, set up the mapping relations of each parameter to artificial mark, and with the parameter preservation that obtains, as the mapping model of the last machine scoring of each parameter, this process is the process that a standard is adjusted.These parameters and model will play the effect of adjusting and proofreading and correct.
Oral English Practice translation points-scoring system, this system comprises a scoring part, scoring process may further comprise the steps:
(f) speech recognition:
F.1 according to tested people's sex and topic type, select corresponding pronunciation dictionary, acoustic model, language model;
F.2 obtain recognition result by big vocabulary continuous speech voice recognition device;
F.3 recognizer is exported the confidence level of each word among the result simultaneously, starting and ending time, and the confidence level of this each phoneme of word, starting and ending time.Recognition result for recognizer output refuses to know function, because the language model of identification framework relatively is partial to the correct translation content, so for translation error, random situation about translating, occur sometimes being identified as correct translation vocabulary, causes the virtual height of scoring.Therefore, system has used and has refused to know function, uses the degree of confidence technology of discerning to suppress the generation of this situation.
(g) Interpreter's obtaining of feature of marking:
G.1 be weighted by the recognition result and the matching degree in indication model answer storehouse d.1 and obtain matching degree, weighting is obtained the feature of matching degree as system; The matching degree of described recognition result and model answer is: to each sense-group of translation topic type, at first the multidigit expert is translated content and generate a model answer network, in test process, use the technology of Dynamic matching, search the content that Interpreter's recognition result and this sense-group mate most, with the feature of matching degree as system.
G.2 mate by recognition result and indication BLEU model d.2, calculate the matching degree under the BLEU pattern, the degree of the coupling under the BLEU pattern a feature as system; The BLEU model that described recognition result and model answer obtain, the matching degree of calculating under the BLEU pattern is: to each translation topic type, according to the model answer that expertise is put in order out, train the BLEU model, according to Interpreter's recognition result, calculate BLEU Model Matching degree then.
G.3 the lexicon grammar vector storehouse model that obtains by recognition result and each grade cypher text of indication d.3 carries out classification and differentiates, go out matching degree according to classification situation and classification distance calculation, with the matching degree of classification situation and classification distance a feature as system; The lexicon grammar vector model that described recognition result and each grade cypher text obtain is: the text of translating out according to Interpreter's expertise 1 and each grade examinee 2, generate the lexicon grammar vector of each grade, then according to Interpreter's recognition result, select a vector that mates most, provide matching degree simultaneously.
G.4 obtain current tested personnel's the fluent situation of partial by word speed, the voice quality that calculates recognition result, with the feature of the fluent situation of partial as system; Tested personnel's voice quality, pronunciation fluency are to measure on model answer network matching content, with the correct translation irrelevant contents on do not pass judgment on, so voice quality and fluency are just at the tested personnel that possess certain Interpreter's ability.
The feature that comprehensive g mentions by the standard adjustment model of using e to obtain, obtains finally scoring, and provides feedback opinion.
Fig. 2 is the process flow diagram of present embodiment, as shown in the figure, may further comprise the steps:
Step 101 is carried out speech signal collection, and the analog-signal transitions that tested personnel are pronounced is a digital signal, and is kept in the computing machine.
Step 102 divides frame to handle to the digital signal of voice, and every frame extraction energy, and the MFCC parameter is totally 39 dimensional features.In the present embodiment, this feature adopts the prior art means to extract, and has instrument HCopy to extract 39 dimensional features in Hidden Makov Model Toolkit 3.4 versions as univ cambridge uk's issue.The additive method that the technical field personnel knew under the extraction of certain above information was also available obtains.
Step 105 according to the feature that step 104 obtains, uses corresponding forecast model to calculate final scoring.Forecast model can be that the method that linear regression forecast model, SVM forecast model, neural network prediction model or other affiliated technical field personnel know realizes.
Step 106 adjusts this test according to above result, passes judgment on, and gives tested personnel some suggestion feedbacks that pronunciation, vocabulary use, sentence pattern use.
The above; only be the embodiment among the present invention; but protection scope of the present invention is not limited thereto; anyly be familiar with the people of this technology in the disclosed technical scope of the present invention; can understand conversion or the replacement expected; all should be encompassed in of the present invention comprising within the scope, therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.
Claims (12)
1. method of using computing machine that spoken translation quality is marked, it is characterized in that: the speech recognition technology that comprehensively uses a computer, sound pronunciation assessment technology, text translation quality determination technology obtain Interpreter's quality of tested personnel, provide feedback opinion simultaneously, it is as follows to comprise step:
Step 1: the database of collecting and establish tested personnel's group characteristic;
Step 2: on Basis of Database, use big vocabulary continuous speech acoustic training model platform, obtain acoustic model;
Step 3: each translation topic type is collected corresponding expertise and cypher text language material, in order to generate language model, scoring characteristic model and the standard adjustment model that recognition system needs;
Step 4: use above-mentioned model, the output recognition result of integrated voice recognizer and linguistics treatment mechanism are exported the score of tested personnel Interpreter quality, and are provided feedback opinion.
2. method according to claim 1 is characterized in that: comprise that also acoustic model, language model and the scoring characteristic model that will obtain are saved in the system, each existing model of only need reloading that uses does not need training pattern again.
3. method according to claim 1, it is characterized in that: set up as follows at the speech database usage policy of tested personnel's group characteristic: the branch sex, seek a collection of suitable crowd according to tested personnel's age distribution and also carry out voice recording according to the recording script of the phoneme balance that designs.
4. method according to claim 2 is characterized in that: the step that obtains language model is as follows:
Step 311: each translation topic type is collected the relevant corpus of text of topic, and training obtains corresponding N gram language model;
Step 312: adopt the training of common english text to obtain general N gram language model, be used to increase the identification compatibility of dialogue outlying content;
Step 313: the N gram language model of each translation topic type correspondence is by obtaining the final language model of its appropriate translation topic type with general N gram language model interpolation.
5. method according to claim 2 is characterized in that: obtain the scoring characteristic model and be to use a plurality of scoring Feature Fusion, it is as follows to obtain a plurality of scoring feature concrete steps:
Step 321: the matching degree weighting of recognition result and model answer network is obtained matching degree;
Step 332: the BLEU model with recognition result and model answer obtain, calculate the matching degree under the BLEU pattern;
Step 323: the lexicon grammar vector model that recognition result and each grade cypher text are obtained carries out classification to be differentiated, and goes out matching degree according to classification situation and classification distance calculation;
Step 324: calculate pronunciation accuracy, the pronunciation fluency of recognition result, obtain the current tested personnel situation of pronouncing.
6. method according to claim 5, it is characterized in that: the matching degree of described recognition result and model answer network is: at first the multidigit expert is translated content and generate a model answer network, in test process, use the technology of Dynamic matching, search the content that this sense-group of Interpreter's recognition result and translation topic type mates most, with the feature of matching degree as system.
7. method according to claim 5, it is characterized in that: the matching degree under the BLEU Model Calculation BLEU pattern that described recognition result and model answer obtain is: to each translation topic type, the model answer of putting in order out according to expertise, train the BLEU model, according to Interpreter's recognition result, calculate BLEU Model Matching degree then.
8. method according to claim 5, it is characterized in that: the lexicon grammar vector model that described recognition result and each grade cypher text obtain is: the text of translating out according to expertise and each grade examinee, generate the lexicon grammar vector of each grade, then according to Interpreter's recognition result, select a vector that mates most, provide matching degree simultaneously.
9. method according to claim 1, it is characterized in that, also comprise: use the expert of the existing examination paper knowledge of giving a mark to carry out the adjustment of machine marking standard in order to improve system performance, described every class Interpreter's topic type is collected the M road by tested personnel's examination question, provide scoring by L expert, and with the artificial scoring of expert's average mark as the per pass topic, introduce then based on forecast method, set up the mapping relations of each parameter to artificial mark, and with the parameter preservation that obtains, as the mapping model of the last machine scoring of each parameter, this process is the process that a standard is adjusted.
10. method according to claim 1 is characterized in that: have for the recognition result of speech recognition device output and refuse to know function, use the degree of confidence technology of identification to suppress translation error, disorderly translation is identified as correct translation vocabulary.
11. method according to claim 1 is characterized in that: described tested personnel's voice quality, pronunciation fluency are to measure on model answer network matching content, are used at the tested personnel that possess certain Interpreter's ability.
12. as the described method of claim 1-11, can be used for using SET or Oral Training software Interpreter, picture talk, see speak spoken language scoring with similar topic type of video; Can also apply to the right spoken quality score of Chinese-English, English-Chinese, Chinese-French and various language.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008102266733A CN101739867B (en) | 2008-11-19 | 2008-11-19 | Method for scoring interpretation quality by using computer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2008102266733A CN101739867B (en) | 2008-11-19 | 2008-11-19 | Method for scoring interpretation quality by using computer |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101739867A true CN101739867A (en) | 2010-06-16 |
| CN101739867B CN101739867B (en) | 2012-03-28 |
Family
ID=42463292
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2008102266733A Active CN101739867B (en) | 2008-11-19 | 2008-11-19 | Method for scoring interpretation quality by using computer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101739867B (en) |
Cited By (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102034475A (en) * | 2010-12-08 | 2011-04-27 | 中国科学院自动化研究所 | Method for interactively scoring open short conversation by using computer |
| CN102930866A (en) * | 2012-11-05 | 2013-02-13 | 广州市神骥营销策划有限公司 | Evaluation method for student reading assignment for oral practice |
| CN103729349A (en) * | 2013-12-23 | 2014-04-16 | 武汉传神信息技术有限公司 | Analyzing method for affecting factors on translation quality |
| CN103810158A (en) * | 2012-11-07 | 2014-05-21 | 中国移动通信集团公司 | Speech-to-speech translation method and device |
| CN104050160A (en) * | 2014-03-12 | 2014-09-17 | 北京紫冬锐意语音科技有限公司 | Machine and human translation combined spoken language translation method and device |
| CN104516870A (en) * | 2013-09-29 | 2015-04-15 | 北大方正集团有限公司 | Translation check method and system |
| CN104952444A (en) * | 2015-04-27 | 2015-09-30 | 桂林电子科技大学 | Chinese oral English quality assessment method independent from text |
| CN106356054A (en) * | 2016-11-23 | 2017-01-25 | 广西大学 | Method and system for collecting information of agricultural products based on voice recognition |
| CN107885730A (en) * | 2017-09-25 | 2018-04-06 | 沈阳航空航天大学 | Translation knowledge method for distinguishing validity under more interpreter's patterns |
| CN108154735A (en) * | 2016-12-06 | 2018-06-12 | 爱天教育科技（北京）有限公司 | Oral English Practice assessment method and device |
| CN108537246A (en) * | 2018-02-28 | 2018-09-14 | 成都优译信息技术股份有限公司 | A kind of method and system that parallel corpora is classified by translation quality |
| CN109118113A (en) * | 2018-08-31 | 2019-01-01 | 传神语联网网络科技股份有限公司 | ETM framework and word move distance |
| CN109214616A (en) * | 2017-06-29 | 2019-01-15 | 上海寒武纪信息科技有限公司 | A kind of information processing unit, system and method |
| CN109241519A (en) * | 2018-06-28 | 2019-01-18 | 平安科技（深圳）有限公司 | Environmental Evaluation Model acquisition methods and device, computer equipment and storage medium |
| CN109979257A (en) * | 2019-04-27 | 2019-07-05 | 深圳市数字星河科技有限公司 | A method of partition operation is carried out based on reading English auto-scoring and is precisely corrected |
| CN110085256A (en) * | 2019-03-21 | 2019-08-02 | 视联动力信息技术股份有限公司 | Information processing method and device |
| CN110245860A (en) * | 2019-06-13 | 2019-09-17 | 桂林电子科技大学 | A method of the automatic scoring based on Virtual Experiment Platform Based |
| CN110674871A (en) * | 2019-09-24 | 2020-01-10 | 北京中科凡语科技有限公司 | Translation-oriented automatic scoring method and automatic scoring system |
| CN110718101A (en) * | 2018-07-12 | 2020-01-21 | 华创云科(深圳)教育科技有限公司 | Education system and method based on virtual reality technology and artificial intelligence technology |
| CN111031232A (en) * | 2019-04-24 | 2020-04-17 | 广东小天才科技有限公司 | Dictation real-time detection method and electronic equipment |
| WO2020140607A1 (en) * | 2019-01-04 | 2020-07-09 | 平安科技（深圳）有限公司 | Voice signal processing method and device, and computer readable storage medium |
| CN111639217A (en) * | 2020-05-12 | 2020-09-08 | 广东小天才科技有限公司 | Spoken language rating method, terminal device and storage medium |
| CN112116839A (en) * | 2020-08-14 | 2020-12-22 | 爱语科技(广州)有限公司 | Language learning interaction method, system and storage medium based on semantic feature symbols |
| CN112562640A (en) * | 2020-12-01 | 2021-03-26 | 北京声智科技有限公司 | Multi-language speech recognition method, device, system and computer readable storage medium |
| CN113539246A (en) * | 2021-08-20 | 2021-10-22 | 北京房江湖科技有限公司 | Speech recognition method and device |
| CN113674764A (en) * | 2021-08-20 | 2021-11-19 | 广东外语外贸大学 | Interpretation evaluation method, system and equipment based on bidirectional cyclic neural network |
| US11656910B2 (en) | 2017-08-21 | 2023-05-23 | Shanghai Cambricon Information Technology Co., Ltd | Data sharing system and data sharing method therefor |
| US11687467B2 (en) | 2018-04-28 | 2023-06-27 | Shanghai Cambricon Information Technology Co., Ltd | Data sharing system and data sharing method therefor |
| US11726844B2 (en) | 2017-06-26 | 2023-08-15 | Shanghai Cambricon Information Technology Co., Ltd | Data sharing system and data sharing method therefor |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2510663A1 (en) * | 2001-12-17 | 2003-06-26 | Neville Jayaratne | A real time translator and method of performing real time translation of a plurality of spoken word languages |
| CN1892724A (en) * | 2005-07-04 | 2007-01-10 | 李杰波 | Foreigh-language oral-speech computer teaching system |
| EP2511833B1 (en) * | 2006-02-17 | 2020-02-05 | Google LLC | Encoding and adaptive, scalable accessing of distributed translation models |
-
2008
- 2008-11-19 CN CN2008102266733A patent/CN101739867B/en active Active
Cited By (41)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102034475B (en) * | 2010-12-08 | 2012-08-15 | 安徽科大讯飞信息科技股份有限公司 | Method for interactively scoring open short conversation by using computer |
| CN102034475A (en) * | 2010-12-08 | 2011-04-27 | 中国科学院自动化研究所 | Method for interactively scoring open short conversation by using computer |
| CN102930866A (en) * | 2012-11-05 | 2013-02-13 | 广州市神骥营销策划有限公司 | Evaluation method for student reading assignment for oral practice |
| CN102930866B (en) * | 2012-11-05 | 2014-05-21 | 广州市神骥营销策划有限公司 | Evaluation method for student reading assignment for oral practice |
| CN103810158A (en) * | 2012-11-07 | 2014-05-21 | 中国移动通信集团公司 | Speech-to-speech translation method and device |
| CN104516870B (en) * | 2013-09-29 | 2018-08-07 | 北大方正集团有限公司 | A kind of translation inspection method and its system |
| CN104516870A (en) * | 2013-09-29 | 2015-04-15 | 北大方正集团有限公司 | Translation check method and system |
| CN103729349A (en) * | 2013-12-23 | 2014-04-16 | 武汉传神信息技术有限公司 | Analyzing method for affecting factors on translation quality |
| CN104050160B (en) * | 2014-03-12 | 2017-04-05 | 北京紫冬锐意语音科技有限公司 | Interpreter's method and apparatus that a kind of machine is blended with human translation |
| CN104050160A (en) * | 2014-03-12 | 2014-09-17 | 北京紫冬锐意语音科技有限公司 | Machine and human translation combined spoken language translation method and device |
| CN104952444B (en) * | 2015-04-27 | 2018-07-17 | 桂林电子科技大学 | A kind of Chinese's Oral English Practice method for evaluating quality that text is unrelated |
| CN104952444A (en) * | 2015-04-27 | 2015-09-30 | 桂林电子科技大学 | Chinese oral English quality assessment method independent from text |
| CN106356054A (en) * | 2016-11-23 | 2017-01-25 | 广西大学 | Method and system for collecting information of agricultural products based on voice recognition |
| CN108154735A (en) * | 2016-12-06 | 2018-06-12 | 爱天教育科技（北京）有限公司 | Oral English Practice assessment method and device |
| US11726844B2 (en) | 2017-06-26 | 2023-08-15 | Shanghai Cambricon Information Technology Co., Ltd | Data sharing system and data sharing method therefor |
| CN109214616A (en) * | 2017-06-29 | 2019-01-15 | 上海寒武纪信息科技有限公司 | A kind of information processing unit, system and method |
| US11537843B2 (en) | 2017-06-29 | 2022-12-27 | Shanghai Cambricon Information Technology Co., Ltd | Data sharing system and data sharing method therefor |
| US11656910B2 (en) | 2017-08-21 | 2023-05-23 | Shanghai Cambricon Information Technology Co., Ltd | Data sharing system and data sharing method therefor |
| CN107885730A (en) * | 2017-09-25 | 2018-04-06 | 沈阳航空航天大学 | Translation knowledge method for distinguishing validity under more interpreter's patterns |
| CN108537246A (en) * | 2018-02-28 | 2018-09-14 | 成都优译信息技术股份有限公司 | A kind of method and system that parallel corpora is classified by translation quality |
| US11687467B2 (en) | 2018-04-28 | 2023-06-27 | Shanghai Cambricon Information Technology Co., Ltd | Data sharing system and data sharing method therefor |
| CN109241519A (en) * | 2018-06-28 | 2019-01-18 | 平安科技（深圳）有限公司 | Environmental Evaluation Model acquisition methods and device, computer equipment and storage medium |
| CN110718101A (en) * | 2018-07-12 | 2020-01-21 | 华创云科(深圳)教育科技有限公司 | Education system and method based on virtual reality technology and artificial intelligence technology |
| CN109118113B (en) * | 2018-08-31 | 2021-08-10 | 传神语联网网络科技股份有限公司 | ETM architecture and word-shifting distance |
| CN109118113A (en) * | 2018-08-31 | 2019-01-01 | 传神语联网网络科技股份有限公司 | ETM framework and word move distance |
| WO2020140607A1 (en) * | 2019-01-04 | 2020-07-09 | 平安科技（深圳）有限公司 | Voice signal processing method and device, and computer readable storage medium |
| CN110085256A (en) * | 2019-03-21 | 2019-08-02 | 视联动力信息技术股份有限公司 | Information processing method and device |
| CN111031232A (en) * | 2019-04-24 | 2020-04-17 | 广东小天才科技有限公司 | Dictation real-time detection method and electronic equipment |
| CN109979257A (en) * | 2019-04-27 | 2019-07-05 | 深圳市数字星河科技有限公司 | A method of partition operation is carried out based on reading English auto-scoring and is precisely corrected |
| CN109979257B (en) * | 2019-04-27 | 2021-01-08 | 深圳市数字星河科技有限公司 | Method for performing accurate splitting operation correction based on English reading automatic scoring |
| CN110245860B (en) * | 2019-06-13 | 2022-08-23 | 桂林电子科技大学 | Automatic scoring method based on virtual experiment platform |
| CN110245860A (en) * | 2019-06-13 | 2019-09-17 | 桂林电子科技大学 | A method of the automatic scoring based on Virtual Experiment Platform Based |
| CN110674871B (en) * | 2019-09-24 | 2023-04-07 | 北京中科凡语科技有限公司 | Translation-oriented automatic scoring method and automatic scoring system |
| CN110674871A (en) * | 2019-09-24 | 2020-01-10 | 北京中科凡语科技有限公司 | Translation-oriented automatic scoring method and automatic scoring system |
| CN111639217A (en) * | 2020-05-12 | 2020-09-08 | 广东小天才科技有限公司 | Spoken language rating method, terminal device and storage medium |
| CN112116839A (en) * | 2020-08-14 | 2020-12-22 | 爱语科技(广州)有限公司 | Language learning interaction method, system and storage medium based on semantic feature symbols |
| CN112562640A (en) * | 2020-12-01 | 2021-03-26 | 北京声智科技有限公司 | Multi-language speech recognition method, device, system and computer readable storage medium |
| CN112562640B (en) * | 2020-12-01 | 2024-04-12 | 北京声智科技有限公司 | Multilingual speech recognition method, device, system, and computer-readable storage medium |
| CN113674764A (en) * | 2021-08-20 | 2021-11-19 | 广东外语外贸大学 | Interpretation evaluation method, system and equipment based on bidirectional cyclic neural network |
| CN113539246B (en) * | 2021-08-20 | 2022-10-18 | 贝壳找房(北京)科技有限公司 | Voice recognition method and device |
| CN113539246A (en) * | 2021-08-20 | 2021-10-22 | 北京房江湖科技有限公司 | Speech recognition method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101739867B (en) | 2012-03-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101739867B (en) | Method for scoring interpretation quality by using computer | |
| CN101740024B (en) | Method for automatic evaluation of spoken language fluency based on generalized fluency | |
| CN102354495B (en) | Testing method and system of semi-opened spoken language examination questions | |
| Saz et al. | Tools and technologies for computer-aided speech and language therapy | |
| CN101751919B (en) | Spoken Chinese stress automatic detection method | |
| CN103928023B (en) | A kind of speech assessment method and system | |
| CN102034475B (en) | Method for interactively scoring open short conversation by using computer | |
| CN106782603B (en) | Intelligent voice evaluation method and system | |
| CN101105939A (en) | Sonification guiding method | |
| CN102426834B (en) | Method for testing rhythm level of spoken English | |
| CN110415725B (en) | Method and system for evaluating pronunciation quality of second language using first language data | |
| Meng et al. | Development of automatic speech recognition and synthesis technologies to support Chinese learners of English: The CUHK experience | |
| Athanaselis et al. | Making assistive reading tools user friendly: A new platform for Greek dyslexic students empowered by automatic speech recognition | |
| Zhang et al. | Using computer speech recognition technology to evaluate spoken English. | |
| CN109300339A (en) | A kind of exercising method and system of Oral English Practice | |
| Koudounas et al. | Italic: An italian intent classification dataset | |
| Liu et al. | AI recognition method of pronunciation errors in oral English speech with the help of big data for personalized learning | |
| Hönig | Automatic assessment of prosody in second language learning | |
| Huang et al. | English mispronunciation detection based on improved GOP methods for Chinese students | |
| Zechner et al. | Automatic scoring of children’s read-aloud text passages and word lists | |
| Li et al. | English sentence pronunciation evaluation using rhythm and intonation | |
| Delmonte | Prosodic tools for language learning | |
| KR20130067854A (en) | Apparatus and method for language model discrimination training based on corpus | |
| Wang et al. | Mispronunciation detection based on cross-language phonological comparisons | |
| Wang et al. | Automatic generation and pruning of phonetic mispronunciations to support computer-aided pronunciation training. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20190724 Address after: 100190 52513, 5th floor, 6 Nansanjie, Zhongguancun, Haidian District, Beijing Patentee after: BEIJING ZIDONG COGNITIVE TECHNOLOGY Co.,Ltd. Address before: 100080 Zhongguancun East Road, Beijing, No. 95, No. Patentee before: Institute of Automation, Chinese Academy of Sciences |
|
| CP01 | Change in the name or title of a patent holder | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 100190 No. 52513, 5th floor, No. 6, South 3rd Street, Zhongguancun, Haidian District, Beijing Patentee after: Beijing Hongtu Cognitive Technology Co.,Ltd. Address before: 100190 No. 52513, 5th floor, No. 6, South 3rd Street, Zhongguancun, Haidian District, Beijing Patentee before: BEIJING ZIDONG COGNITIVE TECHNOLOGY Co.,Ltd. |
|
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20230421 Address after: 100190 automation building, No. 95, Zhongguancun East Road, Haidian District, Beijing Patentee after: BEIJING ZHONGZI SCIENCE AND TECHNOLOGY BUSINESS INCUBATOR CO.,LTD. Address before: 100190 No. 52513, 5th floor, No. 6, South 3rd Street, Zhongguancun, Haidian District, Beijing Patentee before: Beijing Hongtu Cognitive Technology Co.,Ltd. |