Polyphone Recognition Using Neural Networks

Authors:
Lishu Li;Qinghua Chen;Jiawei Chen;Fukang Fang
Affiliations:
Department of Systems Science, School of Management, Beijing Normal University, Beijing, P.R. China 100875;Department of Systems Science, School of Management, Beijing Normal University, Beijing, P.R. China 100875;Department of Systems Science, School of Management, Beijing Normal University, Beijing, P.R. China 100875;Institute of Non-equilibrium Systems, Beijing Normal University, Beijing, P.R. China 100875 and State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P.R ...
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

Citing 4
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Abstract

In this paper, we explore the recognition of polyphone. The cognition process is complex, which needs other additional information, otherwise it may cause uncertainty in decision. Recent research is almost focused on phonetics, while we plan to explore the question with neural networks. H. Haken used synergetic neural network to discuss the recognition of ambivalent patterns and the evolution equation of order parameters can interpret the oscillation in perception. Based on his idea, we argue that the process of cognition is phase transformation. Then we apply Hopfield network (associative memory network) with depressing synapse to simulate the recognition process. With our model, a Chinese polyphone is demonstrated. The result supports our interpretation strongly.