Third International Conference on Spoken Language Processing (ICSLP 94)

Yokohama, Japan
September 18-22, 1994

A Novel Fuzzy Partition Model Architecture for Classifying Dynamic Patterns

Yoshinaga Koto, Shigeru Katagiri

ATR Interpreting Telecommunications Research Laboratories, Kyoto, Japan

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.

Full Paper

Bibliographic reference.  Koto, Yoshinaga / Katagiri, Shigeru (1994): "A novel fuzzy partition model architecture for classifying dynamic patterns", In ICSLP-1994, 1551-1554.