Speech Recognition of Thai Digits using Modified Cross-Correlation Neural Network

A. Thammano and N. Klomiam (Thailand)


Neural Network, Cross-correlation, Classification, DataMining, Speech Recognition


In this paper, Modified Cross-Correlation Neural Network (MCCNN), which is an extension of Cross-Correlation Neural Network (CCNN) [1], is proposed. Unlike the CCNN, which utilizes the normalized cross-correlation at zero lag as a choice function to determine the winning cluster node, MCCNN uses the maximum of the normalized cross-correlation instead. In this work, spoken Thai digits (0-9) are used as the experimental data. The performance of MCCNN, CCNN and other two well known algorithms, Back-propagation and Fuzzy ARTMAP, are compared. The results show that MCCNN has the best performance with respect to the recognition rate.

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