Integrated training for spotting Japanese phonemes using large phonemic time-delay neural networks

A description of integrated training methods of time-delay neural networks (TDNNs) for spotting all Japanese phonemes is presented. The time-shift invariance of the TDNN is confirmed by the use of 2620 testing words, with 95.8% of the phonemes correctly spotted. These experiments show that the spotting performance of the TDNN is high, while types of tendencies toward insertion and deletion errors are clarified. To reduce these spotting errors, integrated training methods using various training token positions are proposed. These methods allow the TDNN to correctly spot phonemes at a rate of 98.0% and also make it possible to realize large-vocabulary, vocabulary-independent speech recognition. To verify this, large-vocabulary speech recognition with 5240 common Japanese words was performed using a predictive LR parser. Recognition rates of 92.6% and 97.6% were obtained for the first and second choices respectively