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Perceptive, Non-linear Speech Processing and Spiking Neural Networks

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Nonlinear Speech Modeling and Applications (NN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3445))

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Abstract

Source separation and speech recognition are very difficult in the context of noisy and corrupted speech. Most conventional techniques need huge databases to estimate speech (or noise) density probabilities to perform separation or recognition. We discuss the potential of perceptive speech analysis and processing in combination with biologically plausible neural network processors. We illustrate the potential of such non-linear processing of speech on a source separation system inspired by an Auditory Scene Analysis paradigm. We also discuss a potential application in speech recognition.

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Authors and Affiliations

  1. Université de Sherbrooke,  

    Jean Rouat, Ramin Pichevar & Stéphane Loiselle

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  1. Jean Rouat
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  2. Ramin Pichevar
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  3. Stéphane Loiselle
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Editor information

Editors and Affiliations

  1. CNRS LTCI/TSI Paris, 46 rue Barrault, 75634, Paris Cedex 13, France

    Gérard Chollet

  2. Department of Psychology, Second University of Naples, and IIASS, Via Pellegrino 19, 84019, Vietri sul Mare, SA, Italy

    Anna Esposito

  3. Escola Universitària Politècnica de Mataró, Universitat Politècnica de Catalunya, Barcelona, Spain

    Marcos Faundez-Zanuy

  4. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi, SA, Italy

    Maria Marinaro

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Rouat, J., Pichevar, R., Loiselle, S. (2005). Perceptive, Non-linear Speech Processing and Spiking Neural Networks. In: Chollet, G., Esposito, A., Faundez-Zanuy, M., Marinaro, M. (eds) Nonlinear Speech Modeling and Applications. NN 2004. Lecture Notes in Computer Science(), vol 3445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11520153_14

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  • DOI: https://doi.org/10.1007/11520153_14

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