The pronunciation variability is an important issue that must be faced with when developing practical automatic spontaneous speech recognition systems .In this paper,the factors that may affect the recognition perform...The pronunciation variability is an important issue that must be faced with when developing practical automatic spontaneous speech recognition systems .In this paper,the factors that may affect the recognition performance are analyzed,inculding those specific to the Chinese language.By studyin the INITIAL/FINAL（IF）characteristics of Chinese language and developing the Bayesian equation,the concepts of generalized INITIAL/FINAL（GIF） and generalized syllable(GS),the GIF modeling and the IF-GIF modeling,as well as the context-dependent pronunciation weighting,are proposed based on a well phonetically transcribed seed database.By using these methods,the Chinese sylable error rate(SER) is reduced by 6.3% and 4.2% compared with the GIF modeling and IF modeling respectively when the language model,such as syllable or word N-gram,is not used.The effectiveness of these methods is also proved when more data without the phonetic transcription are used to refine the acoustic model using the proposed iterative forced-alignment based transcribing(IFABT)method,achieving a 5.7% SER reduction.展开更多
文摘The pronunciation variability is an important issue that must be faced with when developing practical automatic spontaneous speech recognition systems .In this paper,the factors that may affect the recognition performance are analyzed,inculding those specific to the Chinese language.By studyin the INITIAL/FINAL（IF）characteristics of Chinese language and developing the Bayesian equation,the concepts of generalized INITIAL/FINAL（GIF） and generalized syllable(GS),the GIF modeling and the IF-GIF modeling,as well as the context-dependent pronunciation weighting,are proposed based on a well phonetically transcribed seed database.By using these methods,the Chinese sylable error rate(SER) is reduced by 6.3% and 4.2% compared with the GIF modeling and IF modeling respectively when the language model,such as syllable or word N-gram,is not used.The effectiveness of these methods is also proved when more data without the phonetic transcription are used to refine the acoustic model using the proposed iterative forced-alignment based transcribing(IFABT)method,achieving a 5.7% SER reduction.
