Network-based connected digit recognition using vector quantization

This paper describes a network-based approach to speaker-independent connected 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 and the use of gross acoustic features for pruning. Recognition involves finding a minimum quantization distortion path through the network by dynamic programming. The system has been evaluated using a portion of a large multi-dialect database developed by Texas Instruments (TI). Using a baseline network of concatenated independent digit models, string and digit accuracies of 86% and 97% respectively have been obtained.