Fast speech adaptation in linear spectral domain for additive and convolutional noise

In this paper, we propose a transform-based adaptation technique for robust speech recognition in unknown environments. It uses maximum likelihood spectral transform (MLST) algorithm with additive and convolutional noise parameters. Previously many adaptation algorithms have been proposed in the cepstral domain. Though the cepstral domain may be appropriate for the speech recognition, it is difficult to handle environmental noise directly in the cepstral domain. Therefore our approach deals with such noise in the linear spectral domain in which speech is directly affected by the noise. As a result, we can use a small number of noise parameters for fast adaptation. The experiments evaluated on the FFMTIMIT corpus shows promising result with only a small number of adaptation data.