Third International Conference on Spoken Language Processing (ICSLP 94)
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.
Bibliographic reference. Koto, Yoshinaga / Katagiri, Shigeru (1994): "A novel fuzzy partition model architecture for classifying dynamic patterns", In ICSLP-1994, 1551-1554.