Keyword Spotting Based On CTC and RNN For Mandarin Chinese Speech

Abstract

The state-of-the-art acoustic modeling for Keyword Spotting (KWS) systems is mainly based on the hybrid model of Hidden Markov Model (HMM) and Neural Network (NN). However, it is challenging to efficiently train such a hybrid system, since the dependence of the intermediate phonetic representation. Motivated by the end-to-end speech recognition systems, we propose a Mandarin KWS system using the end-to-end method, which directly predict the posterior of phonetic units. The system is based on Connectionist Temporal Classifier (CTC) and Recurrent Neural Network (RNN). The main difference between our system and other CTC-based KWS system is the output alphabet and its corresponded keyword searching mechanism. We adopt Mandarin syllables as the output labels, rather than the phonemes or characters. Extensive experiments are conducted on the Mandarin Chinese speech dataset. Experimental results indicate that: 1) Compared with HMM-based KWS system, the end-to-end KWS system achieves a significant improvement, without any increase of computational cost. 2) Our syllable-based end-to-end KWS system obtains better performance than the state-of-the-art ones based on Chinese context independent (CI) phonemes or Chinese characters.