The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Apr. 29, 2014
Filed:
Oct. 29, 2009
Fang Zheng, Beijing, CN;
Xi Xiao, Beijing, CN;
Linquan Liu, Beijing, CN;
Zhan You, Beijing, CM;
Wenxiao Cao, Beijing, CN;
Makoto Akabane, Tokyo, JP;
Ruxin Chen, Redwood City, CA (US);
Yoshikazu Takahashi, Saitama, JP;
Fang Zheng, Beijing, CN;
Xi Xiao, Beijing, CN;
Linquan Liu, Beijing, CN;
Zhan You, Beijing, CM;
Wenxiao Cao, Beijing, CN;
Makoto Akabane, Tokyo, JP;
Ruxin Chen, Redwood City, CA (US);
Yoshikazu Takahashi, Saitama, JP;
Sony Computer Entertainment Inc., Tokyo, JP;
Tsinghua University, Beijing, CN;
Abstract
The present invention relates to a method for modeling a common-language speech recognition, by a computer, under the influence of multiple dialects and concerns a technical field of speech recognition by a computer. In this method, a triphone standard common-language model is first generated based on training data of standard common language, and first and second monophone dialectal-accented common-language models are based on development data of dialectal-accented common languages of first kind and second kind, respectively. Then a temporary merged model is obtained in a manner that the first dialectal-accented common-language model is merged into the standard common-language model according to a first confusion matrix obtained by recognizing the development data of first dialectal-accented common language using the standard common-language model. Finally, a recognition model is obtained in a manner that the second dialectal-accented common-language model is merged into the temporary merged model according to a second confusion matrix generated by recognizing the development data of second dialectal-accented common language by the temporary merged model. This method effectively enhances the operating efficiency and admittedly raises the recognition rate for the dialectal-accented common language. The recognition rate for the standard common language is also raised.