Evaluation of a vector quantization talker recognition system in text independent and text dependent modes

A vector quantization based talker recognition system is described and evaluated. The system is based on constructing highly efficient short-term spectral representations of individual talkers using vector quantization codebook construction techniques. Although the approach is intrinsically text-independent, the system can be easily extended to text-dependent operation for improved performance and security by encoding specified training word utterances to form word prototypes. The system has been evaluated using a 100-talker database of 20,000 spoken digits. In a talker verification mode, average equal-error rate performance of 2.2% for text-independent operation and 0.3% for text-dependent operation is obtained for 7-digit long test utterances.