High performance connected digit recognition, using hidden Markov models

Algorithms for connected-word recognition based on whole-word reference patterns have become increasingly sophisticated and have been shown capable of achieving high recognition performance for small or syntax-constrained moderate-size vocabularies in a speaker-trained mode. An enhanced analysis feature set consisting of both instantaneous and transitional spectral information is used and the hidden-Markov-model-based connected digit recognizer is tested in speaker-trained, multispeaker, and speaker-independent modes. The performance achieved was 0.35, 1.65 and 1.75% string error rates, respectively, for known length strings and 0.78, 2.85 and 2.94% string error rates, respectively, for unknown length strings