On the use of scalar quantization for fast HMM computation

This paper describes an algorithm for reducing the amount of arithmetic operations in the likelihood computation of continuous mixture HMM (CMHMM) with diagonal covariance matrices while retaining high performance. The key points are the use of the scalar quantization of the input observation vector components and table look-up. These make multiplication, squaring and division operations entirely unnecessary in the whole HMM computation (i.e., output probability calculation and trellis/Viterbi computation). It is experimentally proved in an large-vocabulary isolated word recognition task that scalar quantization into no less than 16 levels does not cause significant degradation in the speech recognition performance. Scalar quantization is also utilized in the computation truncation for unlikely distributions; the total number of distribution likelihood computations can be reduced by 66% with only a slight performance degradation. This “multiplication-free” HMM algorithm has high potentiality in speech recognition applications on personal computers