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Mandarin Chinese tone patterns vary in one of the four ways, i.e, (1) high level; (2) rising; (3) low falling and rising; and (4) high falling. The present study is to examine the efficacy of an artificial neural network in recognizing these tone patterns. Speech data were recorded from 12 children (3-6 years of age) and 15 adults. All subjects were native Mandarin Chinese speakers. The fundamental frequencies (F0) of each monosyllabic word of the speech data were extracted with an autocorrelation method. The pitch data(i.e., the F0 contours) were the inputs to a feed-forward backpropagation artificial neural network. The number of inputs to the neural network varied from 1 to 16 and the hidden layer of the network contained neurons that varied from 1 to 16 in number. The output of the network consisted of four neurons representing the four tone patterns of Mandarin Chinese. After being trained with the Levenberg-Marquardt optimization, the neural network was able to successfully classify the tone patterns with an accuracy of about 90% correct for speech samples from both adults and children. The artificial neural network may provide an objective and effective way of assessing tone production in prelingually-deafened children who have received cochlear implants.
Mandarin Chinese tone patterns vary in one of the four ways, ie, (1) high level; (2) rising; (3) low falling and rising; and (4) high falling. The present study is to examine the efficacy of an All of them were native Mandarin Chinese speakers. The fundamental frequencies (F0) of each monosyllabic word of the speech data were extracted with an autocorrelation method. The pitch of data (ie, the F0 contours) were the inputs to a feed-forward backpropagation artificial neural network. The number of inputs to the neural network varied from 1 to 16 and the hidden layer of the network contained neurons that varied from 1 to 16 in number. The output of the network consisted of four neurons representing the four tone patterns of Mandarin Chinese. After being trained with the Levenberg-Marquardt optimization, the neural network was able to successfully class ify the tone patterns with an accuracy of about 90% correct for speech samples from both adults and children. The artificial neural network may provide objective and effective way of assessing tone production in prelingually-deafened children who have received cochlear implants.