Jacobian Adaptation of HMM with Initial Model Selection for Noisy Speech Recognition

Abstract

An extension of Jacobian Adaptation (JA) of HMMs for degraded speech recognition is presented in which appropriate set of initial models is selected from a number of initial-model sets designed for different noise environments. Based on the first order Taylor series approximation in the acoustic feature domain, JA adapts the acoustic model parameters trained in the initial noise environment A to the new environment B much faster than PMC that creates the acoustic models for the target environment from scratch. Despite the advantage of JA to PMC, JA has a theoretical limitation that the change of acoustic parameters from the environment A to B should be small in order that the linear approximation holds. To extend the coverage of JA, the ideas of multiple sets of initial models and their automatic selection scheme are discussed. Speaker-dependent isolated-word recognition experiments are carried out to evaluate the proposed method.