Thai polysyllabic word recognition using fuzzy-neural network

Abstract

A fuzzy-neural network (fuzzy-NN) model was proposed for speaker-independent Thai polysyllabic word recognition. Fuzzy features converted from exact features were used to be input of multilayer perceptron (MLP) neural network. Various fuzzy membership functions on linguistic properties were used for fuzzy conversion and compared together. The binary desired outputs were used during training. 70 Thai words consist of ten numerals, the others were single-syllable, double-syllable and triple-syllable, 20 words in each group, were used for system evaluation. In order to improve recognition accuracy, number of syllable and tonal level detected were conducted for speech preclassification. The Pi fuzzy membership function provided the best recognition accuracy among other functions; trapezoidal, and triangular function. Under an optimal condition, the achieved recognition error rates were 5.6% on dependent test and 6.7% on independent test, which were respectively 3.3% and 3.4% decreasing from the conventional neural network system.