Discrete-Mixture HMMs-based Approach for Noisy Speech Recognition

It is well known that the application of hidden Markov models (HMMs) has led to a dramatic increase of the performance of automatic speech recognition in the 1980s and from that time onwards. In particular, large vocabulary continuous speech recognition (LVCSR) could be realized by using a recognition unit such as phones. A variety of speech characteristics can be modelled by using HMMs effectively. The HMM represents the transition of statistical characteristics by using the state sequence of a Markov chain. Each state of the chain is composed by either a discrete output probability or a continuous output probability distribution. In 1980s, discrete HMM was mainly used as an acoustic model of speech recognition. The SPHINX speech recognition system was developed by K.-F. Lee in the late 1980s (Lee & Hon, 1988). The system was a speaker-independent, continuous speech recognition system based on discrete HMMs. It was evaluated on the 997-word resource management task and obtained a word accuracy of 93% with a bigram language model. After that, comparative investigation between discrete HMM and continuous HMM had been made and then it was concluded that the performance of continuous-mixture HMM overcame that of discrete HMM. Then almost all of recent speech recognition systems use continuous-mixture HMMs (CHMMs) as acoustic models. The parameters of CHMMs can be estimated efficiently under assumption of normal distribution. Meanwhile, the discrete Hidden Markov Models (DHMMs) based on vector quantization (VQ) have a problem that they are effected by quantization distortion. However, CHMMs may unfit to recognize noisy speech because of false assumption of normal distribution. The DHMMs can represent more complicated shapes and they are expected to be useful for noisy speech. This chapter introduces new methods of noise robust speech recognition using discretemixture HMMs (DMHMMs) based on maximum a posteriori (MAP) estimation. The aim of this work is to develop robust speech recognition for adverse conditions which contain both stationary and non-stationary noise. Especially, we focus on the issue of impulsive noise which is a major problem in practical speech recognition system. DMHMM is one type of DHMM frameworks. The method of DMHMM was originally proposed to reduce computation costs in decoding process (Takahashi et al., 1997).