Abstract
The present study evaluates multiple binary classifier model (MBCM) and Gaussian mixture model (GMM) solutions for both automatic speaker verification (ASV) and automatic speaker identification (ASI) problems involving text-independent telephone speech from the King speech database. The MBCM's accuracy is enhanced by selectively removing those classifiers within the model which perform worst (pruning). An unpruned MBCM outperforms a GMM for ASV and speakers taken from within the same dialectic region (San Diego, CA). Once pruned, the MBCM is found to be 2.6 times more accurate than the GMM. For closed set ASI, based on the same data, the MBCM is roughly twice as accurate as the GMM but only after pruning.
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