A robust compensation strategy for extraneous acoustic variations in spontaneous speech recognition.

We propose a robust compensation strategy to deal effectively with extraneous acoustic variations for spontaneous speech recognition. This strategy extends speaker adaptive training, and uses hidden Markov models (HMM) parameter transformations to normalize the extraneous variations in the training...

詳細記述

書誌詳細
出版年:	IEEE Transactions on speech and audio processing 10, 1 (2002).
第一著者:	Hui Jiang
フォーマット:	論文
言語:	English
主題:	Bayesian prediction classification. HMM parameter transformations. HMM system. Compact model. Conversational speech recognition. Extraneous acoustic variations. Generic model. Hidden Markov models. Inter-speaker variability. Maximum likelihood criterion. Prior PDF. Prior probability density functions. Pronunciation variations. Robust compensation. Robust decoding. Speaker adaptive training. Spontaneous speech recognition. Switchboard task. Telephone handset mismatch. Testing phase. Word error rate reduction. Word sequence.