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...

Täydet tiedot

Bibliografiset tiedot
Julkaisussa:	IEEE Transactions on speech and audio processing 10, 1 (2002).
Päätekijä:	Hui Jiang
Aineistotyyppi:	Artikkeli
Kieli:	English
Aiheet:	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.