Speaker-independent Malay plosives recognition based on place of articulation using Neural Networks

The paper aims at investigating the use of Neural Networks in classifying the Malay plosives based on their places of articulation. The approach is quite different from the conventional speech recognition methods, where they classify the speech sounds according to their phonemes, syllables or isolated words. A three-layer Multi-layer Perception (MLP) is used to classify six plosive sounds in various phonetic contexts. The MLP is trained with stochastic Back-propagation (BP), where the weights are updated after presentation of each training token. The speech tokens are sampled at 16 kHz with 16-bit resolution. The proposed method can be used in a speech training system to show the tongue placement based on the speech sounds. The method is able to achieve a promising recognition rate of 90% when tested speaker-independently on adult and children speakers.