Speaker-independent Malay vowel recognition of children using neural networks

Abstract

This paper investigates the use of Neural Networks in recognizing Malay vowels of children in speakerindependent manner. Malay vowels are comprised of /a/, /e/, /?/, /i/, /o/ and /u/. Speech database is collected from 300 Malay children between seven and twelve years old. Each speaker contributes two samples per vowel sound. The speech database is organized equally into training set and test set. The speech sounds are sampled at 20 kHz with 16 bit resolution. A single frame of cepstral coefficients is extracted around the vowel onset point using Linear Predictive Coding. Multi-Layer Perceptron (MLP) with one hidden-layer is used to train and recognize the vowel sounds. The output of the MLP consists of 6 neurons, which correspond to the 6 vowel sounds. Experiments are conducted to determine the optimal signal length of vowels, and hidden neuron number of MLP. A maximum recognition rate of 75.00% is achieved at signal length of 30ms and 35ms.