Network-based isolated digit recognition using vector quantization

Abstract

This paper describes a network-based approach to speaker-independent digit recognition. The digits are modeled by a pronunciation network whose arcs represent classes of acoustic-phonetic segments. Each arc is associated with a matcher for rating an input speech interval as an example of the corresponding segment class. The matchers are based on vector quantization of LPC spectra. Recognition involves finding a minimum quantization distortion path through the network by dynamic programming. The system has been evaluated in an extensive series of speaker-independent isolated digit (one-nine, oh and zero) recognition experiments using a 225-talker. multidialect database developed by Texas Instruments (TI). The best recognizer configurations achieved accuracies of 97-99 percent on the TI database.