Text-dependent speaker-recognition systems based on one-pass dynamic programming algorithm

Abstract

We propose variable-text text-dependent speaker-recognition systems based on the one-pass dynamic programming (DP) algorithm. The key feature of the proposed algorithm is its ability to use multiple templates for each of the words which form the "password' text. The use of multiple templates allows the proposed system to capture the idiosyncratic intra-speaker variability of a word, resulting in significant improvement in the performance. Our algorithm also uses inter-word silence templates to handle continuous speech input. We use the proposed onepass DP algorithm in three speaker-recognition systems, namely, closed-set speaker-identification (CSI), speaker-verification (SV) and open-set speaker-identification (OSI). These systems were evaluated on a 100 speaker and 200 speaker tasks using the TIDIGITS database and with various car noise conditions. The key result of this paper is that the use of multiple templates enhances the performance of all the three systems significantlythe use ofmultiple templates (in comparison to a single template) enhances the CSI performance from 94% to 100%, the SV EER from 1.6% to 0.09% and the OSI EER from 12.3% to 3.5% on a 100 speaker task. We also use the proposed one-pass DP for automatically extracting the multiple templates from continuous speech training data. The performance of the three systems using such automatically extracted multiple templates is as good as with manually extracted templates. Front-end noise suppression enables our systems to deliver robust performance in up to 0 dB car noise.

Constrained temporal structure for text-dependent speaker verification

“…However, two major trends dominate the field of text-dependent speaker verification. Approaches based on dynamic programming have been proposed for tasks where the quantity of speech is limited [13,14,15,16]. They provide a precise modelling of the time constraints but lack the generalization power available with hidden Markov model (HMM) approaches [17].…”