Scalar quantization of features in discrete hidden Markov models

Traditionally, discrete hidden Markov models (DHMM) use vector quantized speech feature vectors. In this paper, we propose scalar quantization of each element of the speech feature vector in the D-HMM formulation. The alteration required in the D-HMM algorithms for this modification is discussed here. Later, a comparison is made between the performance of D-HMM based speech recognizers using scalar and vector quantization of speech features respectively. A speaker independent TIMIT vowel classification experiment is chosen for this task. It is observed that the scalar quantization of features enhances the vowel classification accuracy by 8 to 9 %, compared to VQ based D-HMM. Also, the number of HMM parameters to estimate from a given amount of training data has drastically reduced in the new ide