Normalized minimum-redundancy and maximum-relevancy based feature selection for speaker verification systems

In this paper, an information theoretical approach to select features for speaker recognition systems is proposed. Conventional approaches having a fixed interval of analysis frames are not appropriate to represent dynamically varying characteristics of speech signals. To maximize the speaker-related information varied by the characteristics of speech signals, we propose an information theory based feature selection method where features are selected to have minimum-redundancy with in selected features but maximum-relevancy to training speaker models. Experimental results verify that the proposed method reduces the error rates of speaker verification systems by 27.37 % in NIST 2002 database.