AUTOMATIC SPEECH RECOGNITION USING HIDDEN CONDITIONAL NEURAL FIELDS

Hidden Conditional Random Fields(HCRF) is a very promis-ing approach to model speech. However, because HCRF computes the score of a hypothesis by summing up linearly weighted features, it cannot consider non-linearity among features that will be crucial for speech recognition. In this pa-per, we extend HCRF by incorporating gate function used in neural networks and propose a newmodel called Hidden Con-ditional Neural Fields(HCNF). Differently with conventional approaches, HCNF can be trained without any initial model and incorporate any kinds of features. Experimental results of continuous phoneme recognition on TIMIT core test set and Japanese read speach recognition task using monophone showed that HCNF was superior to HCRF and HMM trained in MPE manner. Index Terms — hidden conditional neural fields, hidden conditional random fields, HMM, speech recognition 1