Classification of Malay speech sounds based on place of articulation and voicing using neural networks

This paper investigates the effectiveness of using neural networks in classifying Malay speech sounds according to their place of articulation and voicing. The system is very different from conventional speech recognition systems, where the systems do not classify the speech sounds into groups of voicing and place of articulation. The system proposed classifies 16 selected Malay syllables into their groups of phonetic features. The Malay syllables are initialized with stops and followed by succeeding vowels. The speech tokens are sampled at 16 kHz with 16-bit resolution. LPC-derived cepstrum is used to extract the speech features. A three-layer multilayer perceptron (MLP) is used to train and recognize the Malay syllables. The system gives an encouraging result, with an average accuracy of 92.92%