In this paper we present the results of speaker verification technology development for use over long distance telephone lines. We describe two large speech databases that were collected to support the development of new speaker verification algorithms. We discuss the results of discriminant analysis techniques which improve the discrimination between true speakers and impostors. We compare the performance of two speaker verification algorithms, one using template based Dynamic Time Warping (DTW) and the other, Hidden Markov Modeling (HMM).
This paper describes a text-dependent method of speaker verification processing which utilizes the statistical correlation between measured features of speech across whole words. The correlation is used in a linear discriminant analysis to define uncorrelated word-level features as a metric. Initial results indicate that this method can significantly reduce the amount of storage necessary for speaker specific speech information. Further, this method provides promise of improved verification performance compared to methods based on HMM state level observation metrics. Since the linear discriminant analysis yields features which are decorrelated over entire words, this method should be more robust to signal distortions which are consistent over the entire utterance.
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