Variational Bayesian learning for speech modeling and enhancement

A new variational Bayesian (VB) learning approach for speech modeling and enhancement is proposed in this paper. We choose time-varying autoregressive process to model clean speech signal and use VB learning to estimate the model parameters and the clean signal in an integrated manner. Our presented algorithm efficiently exploits prior information and statistical structures of speech model and noise characteristic. Furthermore, it can automatically choose the model order and avoid overfitting in the estimation. Experimental results compared with other methods can demonstrate the performance of our algorithm.