Modeling Variance Variation in a Variable Parameter HMM Framework for Noise Robust Speech Recognition

Variance variation with respect to a continuous environment-dependent variable is investigated in this paper in a variable parameter Gaussian mixture HMM (VP-GMHMM) for noisy speech recognition. The variation is modeled by a scaling polynomial applied to the variances in the conventional hidden Markov acoustic models. The maximum likelihood estimation of the scaling polynomial is performed under an SNR quantization approximation. Experiments on the Aurora 2 database show significant improvements by incorporating the variance scaling scheme into the previous VP-GMHMM where only mean variation is considered