The consonant/vowel (C/V) speech classification using high-rank function neural network (HRFNN)
Jiang Minghu, Yuan Baozong, Lin Biqin · 2002
The article provide an improvement of the method of Bendiksen et. al. (1990) adopting the backpropagation (BP) network for voiced/unvoiced speech classification, by using the HRFNN, adapting it to the non-linear pronunciation model. The comparison test has shown that the HRFNN has a 100 times higher training rate than the BP network and the recognition accuracy is better than the BP network. As for the dynamic time-changing characterization of the speech signals and non-right-cross distribution of the C/V features, it was very difficult to search for the accurate CV transforming point in the past. A time-delay HRFNN is put forward, it is very effective for recognition of the CV transforming point, and for the automatic segmentation of continuous speech, it has a fast training rate, high recognition accuracy, and good dynamic characterization. The theory and experiments have shown that the network model is of high robustness.