Cepstral behaviour due to additive noise and a compensation scheme for noisy speech recognition

The speech cepstral coefficients affected by additive noise are investigated. The cepstral vector changes as the level of additive noise increases. The behaviour of cepstral vector change shows that the cepstral vector shrinks in its norm and converges to the cepstral vector of the noise. This nonlinear behaviour of the cepstral vector can be approximated by a simple linear expression. Based on this representation, a model adaptation method is developed using deviation vectors. For every model state mean, a deviation vector is calculated according to the extracted noise spectrum and a pre-defined noise-to-signal ratio. During the pattern matching, an optimal scaling factor for the deviation vector is determined frame by frame, and the scaled deviation vector is added to the state mean of speech models so that the clean speech models are adapted to the noisy environment. Experimental results show that the proposed method is effective for white noise and coloured noise. It also outperforms the weighted projection measure method in experiments