7th International Conference on Spoken Language Processing
September 16-20, 2002
In this paper, we apply the noise adaptive speech recognition for noisy speech recognition in non-stationary noise to the situation that acoustic models are trained from noisy speech. We justify it by that the noise adaptive speech recognition includes iterative processes between a noise parameter estimation step and a model adaptation step, which can possibly do non-linear mapping between the original training space and that for recognition. Experiments were performed on Aurora-2 task with multi-conditional training set which includes noisy utterances. Through experiments, we observed that the noise adaptive speech recognition can have better performance than the baseline system trained from multi-conditional training set without noise adaptive speech recognition.
Bibliographic reference. Yao, Kaisheng / Paliwal, Kuldip K. / Nakamura, Satoshi (2002): "Noise adaptive speech recognition with acoustic models trained from noisy speech evaluated on Aurora-2 database", In ICSLP-2002, 2437-2440.