Robust speech recognition based on stochastic matching

We present a maximum likelihood (ML) stochastic matching approach to decrease the acoustic mismatch between a test utterance Y and a given set of speech hidden Markov models Λ_X so as to reduce the recognition performance degradation caused by possible distortions in the test utterance. This mismatch may be reduced in two ways: (1) by an inverse distortion function F_ν(.) that maps Y into an utterance X which matches better with the models Λ_X, and (2) by a model transformation function G_η(.) that maps Λ_X to the transformed model Λ_Y which matches better with the utterance Y. The functional form of the transformations depends upon our prior knowledge about the mismatch, and the parameters are estimated along with the recognized string in a maximum likelihood manner using the EM algorithm. Experimental results verify the efficacy of the approach in improving the performance of a continuous speech recognition system in the presence of mismatch due to different transducers and transmission channels