Unsupervised non-parametric Bayesian modeling of non-stationary noise for model-based noise suppression

The accurate modeling of non-stationary noise plays an important role in model-based noise suppression for noise robust speech recognition. We have already proposed methods for unsupervised noise modeling with a Gaussian mixture model or a hidden Markov model by using a minimum mean squared error estimate of the noise. However, our previous work fixed the structure of the noise model empirically without any consideration of noise characteristics; thus, optimization of the noise model structure is required if we are to obtain further improvements. Although the Bayesian information criterion (BIC) has been widely used as a conventional approach to model structure estimation, it is not always the optimal criterion. Therefore, this paper presents a way of modeling non-stationary noise with a non-parametric Bayesian approach that estimates the model structure depending on the characteristics of given observations. The proposed method provided improved results for the evaluations of two different speech recognition tasks compared with results obtained using the conventional BIC-based approach.