Acoustic feature diversity and speaker verification

We present a new method for speaker verification that uses the diversity of information from multiple feature representations. The principle behind the method is that certain features are better at recognising certain speakers. Thus, rather than using the same feature representation for all speakers, we use different features for different speakers. During training, we determine the optimal feature for each speaker from candidate features, by measuring information-theoretic criteria. During evalua- tion, verification is performed using the optimal feature of the claimed speaker. Experimental results with four candidate features show that the proposed system outperforms conventional systems that use a single feature or a combination of features. Index Terms: speaker verification, feature selection