Abstract
Neural networks offer the potential of providing massive parallelism, adaptation, and new algorithm approaches to speech recognition. In this communication, we show a new approach to face the problem of speaker-independent isolated word recognition with the Multilayer Perceptron (MLP), trained with Backpropagation algorithm. This approach lies in a preprocessing similar to that used for Kohonen Networks, thus in the context of unsupervised learning, which allows to overcome the temporal alignment problem of word samples and to reduce the number of neurons in the MLPs. As a preliminary result, the performances of MLPs for recognizing sequences of vowels in isolated words, after learning with samples of isolated vowels, are presented.
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© 1991 Springer-Verlag Berlin Heidelberg
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Cañas, A., Ortega, J., Fernández, F.J., Prieto, A., Pelayo, F.J. (1991). An approach to isolated word recognition using multilayer perceptrons. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035912
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DOI: https://doi.org/10.1007/BFb0035912
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