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A hybrid method for continuous-speech recognition which combines hidden Markov models (HMMs) and a connectionist technique called connectionist Viterbi training (CVT) is presented. CVT can be run iteratively and can be applied to large-vocabulary recognition tasks. Successful completion of training the connectionist component of the system, despite the large network size and volume of training data, depends largely on several measures taken to reduce learning time. The system is trained and tested on the TI/NBS speaker-independent continuous-digits database. Performance on test data for unknown-length strings is 98.5% word accuracy and 95.0% string accuracy. Several improvements to the current system are expected to increase these accuracies significantly.>
Franzini et al. (Wed,) studied this question.