I Summary of Best Ti and Td Vq Performance Speaker Recognition Using Hidden Markov Models, Dynamic Time Warping and Vector Quantisation

1 Illustration of the segmentation of the database collected over a period of three months into training and 3 %Error against total number of mixtures for TI ergodic CDHMMs (10 version training) 7 %Error against the number of training versions for a TI 32 element VQ, and 32 mixture single state CDHMM 11 8 %Error against the number of training versions for TD DTW, 8 element VQ and 1 mixture 8 state CDHMM 11 9 DTW text-dependent digit Abstract This paper evaluates continuous density hidden Markov models (CDHMM), dynamic time warping (DTW) and distortion-based vector quantisation (VQ) for speaker recognition, emphasising the performance of each model structure across incremental amounts of training data. Text-independent (TI) experiments are performed with VQ and CDHMMs, and text-dependent (TD) experiments are performed with DTW, VQ and CDHMMs. We show for TI speaker recognition, VQ performs better than an equivalent CDHMM with one training version, but is outperformed by CDHMM when trained with ten training versions. For TD experiments we show that DTW outperforms VQ and CDHMMs for sparse amounts of training data, but with more data, the performance of each model is indistinguishable. The performance of the TD procedures is consistently superior to TI, which is attributed to subdividing the speaker recognition problem into smaller speaker-word problems. We also show a large variation in performance across the diierent digits, concluding that digit zero is the best digit for speaker discrimination.