Phase term modeling for enhanced feature-space VTS

Abstract

This paper proposes a generalization of the Vector Taylor Series (VTS) approach for the compensation of speech feature distortions. It uses a phase term aware representation of the speech distortion model. It considers this term as a Gaussian random vector with unknown parameters in the same manner as it is conventionally done for additive noise. These parameters are estimated by means of the EM-algorithm. The explicit expressions for parameters update are derived. The minimum mean square error (MMSE) estimate of clean speech features is also obtained. Experiments carried out on the Aurora2 and Aurora4 databases show that the proposed approach outperforms the phase-insensitive version of feature-space VTS significantly for both GMM and DNN acoustic models. It is also shown that the combination of the proposed approach with the cepstral mean normalization (CMN) provides additional accuracy gains.