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.
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Li, L., Chen, Q., Chen, J., Fang, F. (2009). Polyphone Recognition Using Neural Networks. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_96
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DOI: https://doi.org/10.1007/978-3-642-01510-6_96
Publisher Name: Springer, Berlin, Heidelberg
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