中国电子科技2006，Vol.4Issue(1)：43-46,4.
Subspace Distribution Clustering HMM for Chinese Digit Speech Recognition
Subspace Distribution Clustering HMM for Chinese Digit Speech Recognition
摘要
Abstract
As a kind of statistical method, the technique of Hidden Markov Model (HMM) is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model (SDCHMM), derived from the Continuous Density Hidden Markov Model (CDHMM), is introduced. With parameter tying, a new method to train SDCHMMs is described. Compared with the conventional training method, an SDCHMM recognizer trained by means of the new method achieves higher accuracy and speed. Experiment results show that the SDCHMM recognizer outperforms the CDHMM recognizer on speech recognition of Chinese digits.关键词
speech recognition/Subspace Distribution Clustering Hidden Markov Model (SDCHMM)/Continuous Density Hidden Markov Model (CDHMM)/parameter tyingKey words
speech recognition/Subspace Distribution Clustering Hidden Markov Model (SDCHMM)/Continuous Density Hidden Markov Model (CDHMM)/parameter tying分类
信息技术与安全科学引用本文复制引用
..Subspace Distribution Clustering HMM for Chinese Digit Speech Recognition[J].中国电子科技,2006,4(1):43-46,4.基金项目
Supported by the National Natural Science Foundation of China (No.60172048) （No.60172048）
