抄録
This paper describes a pairwise discrimination approach using artificial neural networks for robust phoneme recognition and its application to continuous speech recognition. Until now, it is known that classification-type neural networks show poor robustness against the difference in speaking rates between training data and testing data. To improve the robustness, we developed Pairwise Discriminant Time-Delay Neural Net-works (PD-TDNNs) by applying the principle of pair discrimination to a conventional Time-Delay Neural Network. In this approach, pair discrimination scores for all com-binations of two phonemes are calculated by PD-TDNNs, each of which has a less sharp discrimination boundary, and final phoneme candidates are decided by majority decision of the pair discrimination scores. Through phoneme and continuous speech recognition experiments, it was found that this approach performs better than the conventional TDNN.
