A connectionist approach to continuous speech recognition

The authors have applied connectionist learning procedures to speaker-independent continuous recognition, creating a system which has achieved 97% word accuracy and 91% sentence accuracy in preliminary tests on the TI/NBS connected-digits database. The system uses a four-layer back-propagation network with recurrent connections to generate and refine hypotheses about the identity of an utterance over successive intervals. The hypotheses generated by the network are used as input to a Markov-chain-based Viterbi recognizer which produces a final identification of the entire utterance.>