Frame-synchronous stochastic matching based on the Kullback-Leibler information

An acoustic mismatch between a given utterance and a model degrades the performance of the speech recognition process. We choose to model speech by hidden Markov models (HMMs) in the cepstrum domain and the mismatch by a parametric function. In order to reduce the mismatch, one has to estimate the parameters of this function. We present a frame synchronous estimation of these parameters. We show that the parameters can be computed recursively. Thanks to such methods, parameters variations can be tracked. We give general equations and study the particular case of an affine transform. Finally, we report recognition experiments carried out over both PSTN and cellular telephone network to show the efficiency of the method in a real context.