AUTOMATIC ACCENT CLASSIFICATION USING ARTIFICIAL NEURAL NETWORKS
C.S. Blackburn, J.P. Vonwiller and R.W. King
We describe the development and performance of an automatic English accent classification system to discriminate between the speech of subjects whose first language is Arabic, Chinese and Australian English. The system operates on speech samples of arbitrary duration. The classification is performed in stages. A broad phonetic class segmenter divides incoming speech into one of voiced, unvoiced, stop and energy dip. For each of these segment types an artificial neural network is used to classify the accent. The sequence of accent labels from these four networks is examined to obtain a cumulative measure of the accent classification. Tested on a small set of data the system has been found to correctly classify accents as rapidly as a trained phonetician.
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