This paper describes an algorithm for detecting the presence of speech from a particular individual, called the target, while rejecting speech from all other individuals. The algorithm requires samples of training data for the target speaker, as well as for a set of N other speakers, called reference speakers. This data is used to estimate the parameters of a set of speaker-pair discriminators. Each discriminator separates the target from one of the reference speakers, producing a positive or negative value for each input frame. The polarity of discriminators is such that positive output values favor the target. After integrating the discriminator outputs over a suitable time interval, the number of positive values is counted and compared with a detection threshold between 0 and N. In an experiment using clean speech materiai, 80% of targets were detected with only 2% of non-targets.
INTRODUCTIONOther speaker verification algorithms have attempted to evaluate the likelihood that the observed data would be produced by the target speaker, without consideration of the likelihood that the data would be produced by other speakers. This approach has led to high variability of performance from one target speaker to another, and lack of robustness with respect to message content and channel conditions. Recently, an algorithm based on estimation of the likelihood ratio[Higg, Higg891 was developed to address this problem. The likelihood-ratio approach compares match scores derived from the target model with match scores derived from the models of other speakers (the reference speaker set). The method described here differs from previous methods, including the likelihood-ratio approach, by modeling speaker-pair distinctions, rather than speakers themselves. The potential advan-
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