Abstract
Some well known theoretical results concerning the universal approximation property of MLP neural networks with one hidden layer have shown that for any function f from [0,1]n to \(\mathcal{R}\), only the output layer weights depend on f. We use this result to propose a network architecture called the predictive Kohonen map allowing to design a new speech features extractor. We give experimental results of this approach on a phonemes recognition task.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Gas, B., Chetouani, M., Zarader, JL., Charbuillet, C. (2005). Predictive Kohonen Map for Speech Features Extraction. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_125
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DOI: https://doi.org/10.1007/11550907_125
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