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International School on Neural Networks, Initiated by IIASS and EMFCSC

NN 2004: Nonlinear Speech Modeling and Applications pp 219–243Cite as

Predictive Connectionist Approach to Speech Recognition

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

Abstract

This tutorial describes a context-dependent Hidden Control Neural Network (HCNN) architecture for large vocabulary continuous speech recognition. Its basic building element, the context-dependent HCNN model, is connectionist network trained to capture dynamics of sub-word units of speech. The described HCNN model belongs to a family of Hidden Markov Model/Multi-Layer Perceptron (HMM/MLP) hybrids, usually referred to as Predictive Neural Networks [1]. The model is trained to generate continuous real-valued output vector predictions as opposed to estimate maximum a posteriori probabilities (MAP) when performing pattern classification. Explicit context-dependent modeling is introduced to refine the baseline HCNN model for continuous speech recognition. The extended HCNN system was initially evaluated on the Conference Registration Database of CMU. On the same task, the HCNN modeling yielded better generalization performance than the Linked Predictive Neural Networks (LPNN). Additionally, several optimizations were possible when implementing the HCNN system. The tutorial concludes with the discussion of future research in the area of predictive connectionist approach to speech recognition.

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Author information

Authors and Affiliations

  1. Interactive Systems Laboratory, University of Ljubljana, Snežniška 5, 1000, Ljubljana, Slovenia

    Bojan Petek

Authors
  1. Bojan Petek
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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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© 2005 Springer-Verlag Berlin Heidelberg

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Petek, B. (2005). Predictive Connectionist Approach to Speech Recognition. 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_10

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  • DOI: https://doi.org/10.1007/11520153_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27441-4

  • Online ISBN: 978-3-540-31886-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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