In last few years connectionist models, mainly-based on multilayered perceptrons, have been used for identifying the speaker identity. To the best of our knowledge however, no significant results have been obtained for speaker verification.

In this paper, we propose a connectionist phoneme-based speaker verification model and give experimental results for assessing its performance. Neural autoassociators are suggested for capturing the speaker's identity. They are trained to reproduce speech frames presented at the input to the output layer. Adequate threshold criteria are proposed for performing rejection. Verification performances were evaluated on DARPA-TIMIT database for /ae/ and /aa/ phonemes in continuous speech, using different thresholds and preprocessing schemes with very promising results.