Second International Conference on Spoken Language Processing (ICSLP'92)
Banff, Alberta, Canada
This paper proposes a Fuzzy Partition Model (FPM) neural network architecture for speaker-independent continuous speech recognition. Generally speaking, conventional TDNN (Time-Delay Neural Network) architecture in its training stage requires much computation time. Nevertheless, an FPM has a rapid training capability that is over two times faster than TDNN's training speed. FPM architecture is combined with an LR-parser and its recognition performance with 278 Japanese phrases is evaluated. The recognition rate of FPM-LR is higher than that of TDNN-LR. This paper also proposes a Multi-FPM-LR method. Using this method, the recognition rate is 77.5% for open speakers.
Bibliographic reference. Fukuzawa, Keiji / Kato, Yoshinaga / Sugiyama, Masahide (1992): "A fuzzy partition model (FPM) neural network architecture for speaker-independent continuous speech recognition", In ICSLP-1992, 1383-1386.