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EURASIP Journal on Audio, Speech, and Music Processing
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Recognition of Noisy Speech: A Comparative Survey of Robust Model Architecture and Feature Enhancement

EURASIP Journal on Audio, Speech, and Music Processing volume 2009, Article number: 942617 (2009) Cite this article

Abstract

Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise inside a car. In contrast to existing works, we aim to improve noise robustness focusing on all major levels of speech recognition: feature extraction, feature enhancement, speech modelling, and training. Thereby, we give an overview of promising auditory modelling concepts, speech enhancement techniques, training strategies, and model architecture, which are implemented in an in-car digit and spelling recognition task considering noises produced by various car types and driving conditions. We prove that joint speech and noise modelling with a Switching Linear Dynamic Model (SLDM) outperforms speech enhancement techniques like Histogram Equalisation (HEQ) with a mean relative error reduction of 52.7% over various noise types and levels. Embedding a Switching Linear Dynamical System (SLDS) into a Switching Autoregressive Hidden Markov Model (SAR-HMM) prevails for speech disturbed by additive white Gaussian noise.

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Authors and Affiliations

  1. Institute for Human-Machine Communication, Technische Universität München (TUM), 80290, Munich, Germany

    Björn Schuller, Martin Wöllmer & Gerhard Rigoll

  2. BMW Group, Forschungs- und Innovationszentrum, Akustik, Komfort und Werterhaltung, 80788, München, Germany

    Tobias Moosmayr

Authors
  1. Björn Schuller
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  2. Martin Wöllmer
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  3. Tobias Moosmayr
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  4. Gerhard Rigoll
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Corresponding author

Correspondence to Björn Schuller.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Schuller, B., Wöllmer, M., Moosmayr, T. et al. Recognition of Noisy Speech: A Comparative Survey of Robust Model Architecture and Feature Enhancement. J AUDIO SPEECH MUSIC PROC. 2009, 942617 (2009). https://doi.org/10.1155/2009/942617

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  • DOI: https://doi.org/10.1155/2009/942617

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