Abstract
Two-weight neural network (TWNN) is described in this paper. Anew dynamic searching algorithm based on Two-weight neural network is presented. And then it is applied to recognize the Continuous Speech of Speaker-Independent. The recognition results can be searched dynamically without endpoint detecting and segmenting. Different feature-space covers are constructed according to different classes of syllables. Compared with the conventional HMM-based method, The trend of recognition results shows that the difference of recognition rates between these two methods decreases as the number of training increases, but the recognition rate of Two-weight neural network is always higher than that of HMM-based. And both of these recognition rates will reach 100% if there are enough training samples.
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© 2005 Springer-Verlag Berlin Heidelberg
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Cao, W., Pan, X., Wang, S. (2005). Continuous Speech Research Based on Two-Weight Neural Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_56
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DOI: https://doi.org/10.1007/11427445_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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