Maximum Likelihood A Priori Knowledge Interpolation-Based Handset Mismatch Compensation for Robust Speaker Identification

Unseen handset mismatch is the major source of performance degradation in speaker identifica-tion in telecommunication environments.To alleviate the problem,a maximum likelihood a priori knowledge interpolation (ML-AKI)-based handset mismatch compensation approach is proposed.It first collects a set of handset characteristics of seen handsets to use as the a priori knowledge for representing the space of handsets.During evaluation the characteristics of an unknown test handset are optimally estimated by in-terpolation from the set of the a pdod knowledge.Experimental results on the HTIMIT database show that the ML-AKI method can improve the average speaker identification rate from 60.0% to 74.6% as compared with conventional maximum a posteriori-adapted Gaussian mixture models.The proposed ML-AKI method is a promising method for robust speaker identification.