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Model-Based Approaches to Handling Uncertainty

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Robust Speech Recognition of Uncertain or Missing Data

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Abstract

A powerful approach for handling uncertainty in observations is to modify the statistical model of the data to appropriately reflect this uncertainty. For the task of noise-robust speech recognition, this requires modifying an underlying “clean” acoustic model to be representative of speech in a particular target acoustic environment. This chapter describes the underlying concepts of model-based noise compensation for robust speech recognition and how it can be applied to standard systems. The chapter will then consider important practical issues. These include i) acoustic environment noise parameter estimation; ii) efficient acoustic model compensation and likelihood calculation; and iii) adaptive training to handle multi-style training data. The chapter will conclude by discussing the limitations of the current approaches and research options to address them.

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Authors and Affiliations

  1. Cambridge University Engineering Department, Trumpington Street, Cambridge, UK

    M. J. F. Gales

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  1. Institute of Communication Acoustics, Ruhr-Universität Bochum, Universitätsstrasse 150, Bochum, 44801, Germany

    Dorothea Kolossa

  2. , Dept. of Communications Engineering, University of Paderborn, Warburger Strasse 100, Paderborn, 33098, Germany

    Reinhold Häb-Umbach

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Gales, M.J.F. (2011). Model-Based Approaches to Handling Uncertainty. In: Kolossa, D., Häb-Umbach, R. (eds) Robust Speech Recognition of Uncertain or Missing Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21317-5_5

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