On the use of dynamic spectral parameters in speech recognition

Spectral dynamics have attracted the attention of researchers in speech recognition for a long time. As part of the speech feature vector they are found to be useful and hence are almost part of any feature extraction algorithm for speech recognition. However, the usual cepstral dynamics do not directly reflect the dynamics of the speech spectrum, as they are extracted from cepstral parameters. In this paper we show that dynamic parameters obtained directly from the speech spectrum can perform better under low-SNR noisy speech conditions, in comparison to the conventional dynamic cepstral parameters. Results on a compact set of the Aurora task have been reported.