Abstract
In this paper, the neural net pattern recognition equations were attempted to apply to speech recognition. The proposed method features a dynamic process of self-organization that has been proved to be successful in recognizing a depth perception in stereoscopic vision. This study showed that the dynamic process was also useful in recognizing human speech. In the processing, input vocal signals are first compared with standard models to measure similarities that are then given to the dynamic process of self-organization. The competitive and cooperative processes are conducted among neighboring input similarities, so that only one winner neuron is finally detected. In a comparative study, it showed that the proposed method outperformed the conventional Hidden Markov Models(HMM) speech recognizer under the same conditions.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, SI. (2006). Neural Net Pattern Recognition Equations with Self-organization for Phoneme Recognition. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_26
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DOI: https://doi.org/10.1007/11760023_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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