ISCA Archive ICSLP 1994
ISCA Archive ICSLP 1994

A novel fuzzy partition model architecture for classifying dynamic patterns

Yoshinaga Koto, Shigeru Katagiri

In speech pattern recognition, there is a clear need to appropriately model the dynamics (variable durational nature) of pattern. This paper discusses a novel neural network solution to this requirement by proposing State-Transition Fuzzy Partition Model (STFPM). STFPM uses an HMM-like state transition structure, of which each state corresponds to one FPM network. The proposed network accordingly inherits all the advantages, such as a fast training and a robust decision, from the original FPM. Evaluations in speaker-dependent phoneme classification tasks clearly demonstrate the utility of this new network classifier.


Cite as: Koto, Y., Katagiri, S. (1994) A novel fuzzy partition model architecture for classifying dynamic patterns. Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994), 1551-1554

@inproceedings{koto94_icslp,
  author={Yoshinaga Koto and Shigeru Katagiri},
  title={{A novel fuzzy partition model architecture for classifying dynamic patterns}},
  year=1994,
  booktitle={Proc. 3rd International Conference on Spoken Language Processing (ICSLP 1994)},
  pages={1551--1554}
}