Compensating for Noise and Mismatch in Speaker Verification Systems Using Approximate Bayesian Inference

Abstract

This paper presents a feature domain approach to the problem of robust speaker verification in noisy acoustic environments. We derive a variational Bayesian algorithm that enhances the log spectra of noisy speech using speaker dependent priors. This algorithm extends prior work by Frey et al. where the Algonquin algorithm was introduced to enhance speech log spectra in order to improve speech recognition in noisy environments. Our work is built on the intuition that speaker dependent priors would work better than priors that attempt to capture global speech properties. Experimental results using the TIMIT data set and the MIT Mobile Device Speaker Verification Corpus (MDSVC) are presented to demonstrate the algorithms performance.