Isolated Digit Speech Recognition in Malay Language Using Neuro-Fuzzy Approach

In this paper we discuss the development and implementation of an automated speaker-independent isolated Malay digit speech recognition system. The system is developed using Neuro-Fuzzy approach that combines the human-like reasoning style of fuzzy systems and the learning and connectionist structure of neural networks. To recognize the Malay speech digits, the endpoint detection algorithm is used to trim the silent duration in speech sample, the Mel Frequency Cepstral Coefficient technique is used to extract speech features, the subtractive clustering algorithm is applied to identify the fuzzy inference system, and the Adaptive Neuro Fuzzy Inference System (ANFIS) is used as a modern classification technique to train in identifying the features of speech. The performance of the system was evaluated by using 630 speech samples for training and testing, and experimental results showed that an overall 85.24% recognition rate was achieved.