Speaker-independent Thai polysyllabic word recognition using hidden Markov model

This correspondence presents a speech recognition system of speaker-independent Thai polysyllabic words. This development is based on the Discrete Hidden Markov Model in conjunction with vector quantization algorithm, also endpoint detection algorithm for syllable endpoint detection and separation, and time normalization algorithm. The 70-Thai word vocabulary are subdivided into four sets comprising single, double, and triple syllabled words, 20 words in each set, and the last set consists of 10-Thai numeric words, zero to nine. The seperated speech training set and testing set are composed of both male and female speakers within the range of 18 to 25 years old. Upon the tonal characteristics of Thai language, the algorithms and the model parameters are modified in order to be applicable to the Thai language. The experiments on the effects of model parameter variations on recognition rate are conducted. The model parameters are number of codebooks, number of model states, and number of training speakers. The results show that the increase in the number of codebook and the number of model states have the major effect on the recognition rates. Also, the number of training speakers has less effect than the others. The average recognition rate of this speaker-independent recognition system is 89.906 percent for 40 speakers testing set using 256-vector codebook of 10-order linear prediction coefficient and 15-state model parameters. The recognition rate of the four sets of words are 86.750 percent for single-syllabled words, 92.375 percent for double-syllabled words, 96.250 percent for triple-syllabled words, and 84.250 percent for the numeric words.