Abstract
Connectionist models, also known as neural networks, have been widely studied during the past few years. Applications concern various tasks in the fields of pattern recognition and signal processing, especially automatic speech recognition. This chapter presents the basic properties of these models and the different problems in the area of speech recognition to which they have been applied so far. The classical models of neural networks are also briefly recalled. We then concentrate on a particular model grounded on neuro-biological data, the cortical column. The characteristics of the model are given and we then present the architectures of two systems based on the cortical column for solving two different problems of speech recognition, i.e. acoustic-phonetic decoding and isolated word recognition.
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References
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© 1992 Springer-Verlag Berlin Heidelberg
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Guyot, F., Alexandre, F., Dingeon, C., Haton, JP. (1992). The cortical column as a model for speech recognition: principles and first experiments. In: Laface, P., De Mori, R. (eds) Speech Recognition and Understanding. NATO ASI Series, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76626-8_29
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DOI: https://doi.org/10.1007/978-3-642-76626-8_29
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