Speaker-specific mapping for text-independent speaker recognition

In this paper, we present the concept of speaker-specific mapping for the task of speaker recognition. The speaker-specific mapping is realized using a multilayer feedforward neural network. In the mapping approach, the aim is to capture the speaker-specific information by mapping a set of parameter vectors specific to linguistic information in the speech, to a set of parameter vectors having linguistic and speaker information. In this study, parameter vectors suitable for speaker-specific mapping are explored. Background normalization for score comparison and network error criterion for frame selection are proposed to improve the performance of the basic system. It is shown that removing the high frequency components of speech results in loss of performance of the speaker verification system. For all the 630 speakers of the TIMIT database, an equal error rate (EER) of 0.5% and 100% identification is achieved by the mapping approach. On a set of 38 speakers of the dialect region "dr1" of NTIMIT database, an EER of 6.6% is obtained.

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