Speaker-independent Malay isolated sounds recognition

This paper simply describes the use of neural networks in recognizing some Malay isolated sounds of Malay children in a speaker-independent manner. The isolated sounds are Malay plosive sounds, which are comprised of /b/, /d/, /g/, /p/, /t/ and /k/. A three-layer Multi-layer Perceptron (MLP) is used to train and recognize the speech sounds. The MLP output layer has an output layer of 6 neurons, which correspond to the 6 isolated plosive sounds. Network parameters such as hidden neuron number and error function, were investigated to achieve the optimal performance of the MLP. The proposed system was able to achieve the highest accuracy of 84.67%.