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Iterative noise and channel estimation under the stochastic matching algorithm framework

In this letter, we introduce an unsupervised iterative algorithm to adapt HMMs trained using clean speech in order to recognize speech corrupted by an additive and a convolutional noise. Both types of noise are considered as stochastic processes that can be modeled using HMMs and can be estimated by applying Sankar's stochastic matching (SM) algorithm successively in the cepstral and in the linear spectral domain. These estimates are derived directly from the given test speech signal and the set of clean speech models, and lead to the estimation of a new set of HMMs that maximize the likelihood of the test signal.