A text-independent speaker recognition method robust against utterance variations

The authors describe a VQ (vector-quantization)-based text-independent speaker recognition method which is robust against utterance variations. Three techniques are introduced to cope with temporal and text-dependent spectral variations. First, either an ergodic hidden Markov model or a voiced/unvoiced decision is used to classify input speech into broad phonetic classes. Second, a new distance measure, the distortion-intersection measure (DIM), is introduced for calculating VQ distortion of input speech compared to speaker-independent codebooks. Third, a normalization method, talker variability normalization (TVN), is introduced. TVN normalizes parameter variation taking both inter- and intra-speaker variability into consideration. The system was tested using utterances of nine speakers recorded over three years. The combination of the three techniques achieves high speaker identification accuracies of 98.5% using only vocal tract information and 99.0% using both vocal tract and pitch information