Text-Dependent Speaker-Recognition Using One-Pass Dynamic Programming Algorithm

We propose a variable-text text-dependent speaker-recognition system based on the one-pass dynamic programming algorithm. The key feature of our 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. Application of the proposed algorithm to two 100-speaker speaker-recognition systems, namely, closed-set speaker-identification (CSI) and speaker-verification (SV), delivers 100% speaker identification accuracy and a speaker-verification EER of 0.09%. The use of multiple templates (in comparison to a single template) enhances the CSI performance from 94% to 100% and the SV EER from 1.6% to 0.09%. Front-end noise suppression enables our systems to deliver robust performance in up to -10 dB car noise