Autoassociator-based models for speaker verification

In this paper, we propose an autoassociator-based connectionist model that turns out to be very useful for problems of pattern verification. The model is based on feedforward networks acting as autoassociators trained to reproduce patterns presented at the input to the output layer. The verification is established on the basis of the distance between the input and the output vectors. We give experimental results for assessing the effectiveness of the model for problems of speech verification. The performances were evaluated on DARPA-TIMIT database in continuous speech, using different thresholds and preprocessing schemes, with very promising results.