A novel HMM model adaptation and compensation method for robust speech recognition
Gengxin Ning, Gang Wei · 2006
It is well known that the performance of a speech recognizer, trained with clean speech database, usually degrades drastically when operating in noisy environments. To make it robust to noise, a novel model adaptation method based on the SNR-dependent non-linear spectral compression MFCC features is proposed, which modifies the HMMs of the clean speech to generate the adaptation models on the basis of the estimated Mel-band SNR and the spectral compression coefficients. In this approach, only the clean speech models are adopted, which not only adapt and compensate the means but also modify the variances, hence it can deal with much lower SNR. In addition, this approach can be used in variable noisy environments. Such methods for adaptive approach will be discussed in detail in this paper. By the adoption of the new adaptive approach, substantial improvement can be observed in recognizing in different noisy environments.