Continuous speech recognition for large vocabulary based on triphone DBN model

To avoid coarticulatory effects in continuous speech recognition, based on word-phone structure dynamic Bayesian network (WP-DBN) model and word-phone-state structure DBN (WPS-DBN) model, context-dependent triphone units are introduced. Two novel single stream DBN models, that is, word-triphone structure DBN (WT-DBN) and word-triphone-state structure DBN (WTS-DBN) models, are proposed for continuous speech recognition. WTS-DBN model is a triphone model and its modeling unit is triphone. It simulates a conventional HMM (hidden markov model) based triphone state-tying. Experimental results in large-vocabulary and clean speech environment show that the speech recognition rates of WTS-DBN model increase 20. 53%, 40.77%, 42.72% and 7.52% than those of the HMM, WT-DBN, WP-DBN and WPS-DBN models.