An RNN-based preclassification method for fast continuous Mandarin speech recognition

Abstract

A novel recurrent neural network-based (RNN-based) front-end preclassification scheme for fast continuous Mandarin speech recognition is proposed. First, an RNN is employed to discriminate each input frame for the three broad classes of initial, final, and silence. A finite state machine (FSM) is then used to classify the input frame into four states including three stable states of initial (I), final (F), and silence (S), and a transient (T) state. The decision is made based on examining whether the RNN discriminates well between classes. We then restrict the search space for the three stable states in the following DP search to speed up the recognition process. The efficiency of the proposed scheme was examined by simulations in which we incorporate it with a hidden Markov model-based (HMM-based) continuous 411 Mandarin based-syllables recognizer. The experimental results showed that it can be used in conjunction with the beam search to greatly reduce the computational complexity of the HMM recognizer while keeping the recognition rate almost undegraded.