Channel and noise adaptation via HMM mixture mean transform and stochastic matching

We present a non-linear model transformation for adapting Gaussian Mixture HMMs using both static and dynamic MFCC observation vectors to additive noise and constant system tilt. This transformation depends upon a few compensation coefficients which can be estimated from channel distorted speech via Maximum-Likelihood stochastic matching. Experimental results validate the effectiveness of the adaptation. We also provide an adaptation strategy which can result in improved performance at reduced computational cost compared with a straightforward implementation of stochastic matching.