Feature adaptation using deviation vector for robust speech recognition in noisy environment
Tai-Hwei Hwang, Lee-Min Lee, Hsiao-Chuan Wang · 2002
When a speech signal is contaminated by additive noise, its cepstral coefficients are assumed to be the functions of the noise power. By using Taylor series expansion with respect to the noise power, the cepstral vector can be approximated by a nominal vector plus the first derivative term. The nominal cepstrum corresponds to the clean speech signal and the first derivative term is a quantity used to adapt the speech feature to a noisy environment. A deviation vector is introduced to estimate the derivative term. The experiments show that the feature adaptation based on the deviation vectors is superior to those of projection based methods.