Abstract—There are many artificial intelligence approaches used in the development of Automatic Speech Recognition (ASR), and hybrid approach is one of them. The common hybrid method in speech recognition is the combination of Hidden Markov Model (HMM) and Artificial Neural Network (ANN). The hybrid HMM/ANN is able to combine the strength of HMM in sequential modeling structure and ANN in pattern classification. Thus, this paper proposed a speaker independent and continuous Malay language speech recogniser by using the hybrid HMM/ANN method. In addition to that, this paper presents a study on Standard Malay’s phonetic and phonology to help in the recognition of Malay words. The CSLU toolkit is utilized for building the recogniser, and the experimental results showed that the proposed HMM/ANN model outperformed the conventional HMM model. The performances of the recognisers are measured in term of word accuracy and sentence accuracy.
Index Terms—Artificial Neural Network, Continuous Speech, Hidden Markov Model, Hybrid HMM/ANN, Malay Language, Speaker Independent, Speech Recognition.
H. F. Ong is with the Faculty of Management and Information
Technology, UCSI University, 56000 Cheras, Kuala Lumpur, Malaysia
A. M. Ahmad is with the Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia (e-mail: email@example.com).
Cite: H. F. Ong and A. M. Ahmad, "Malay Language Speech Recogniser with Hybrid Hidden Markov Model and Artificial Neural Network (HMM/ANN)," International Journal of Information and Education Technology vol. 1, no. 2, pp. 114-119, 2011.