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  • Research Article
  • Open Access

Recognition of Noisy Speech: A Comparative Survey of Robust Model Architecture and Feature Enhancement

  • Björn Schuller1Email author,
  • Martin Wöllmer1,
  • Tobias Moosmayr2 and
  • Gerhard Rigoll1
EURASIP Journal on Audio, Speech, and Music Processing20092009:942617

Received: 28 October 2008

Accepted: 15 February 2009

Published: 24 May 2009


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.


Hide Markov ModelAcousticsSpeech RecognitionAdditive White Gaussian NoiseModel Architecture

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Authors’ Affiliations

Institute for Human-Machine Communication, Technische Universität München (TUM), Munich, Germany
BMW Group, Forschungs- und Innovationszentrum, Akustik, Komfort und Werterhaltung, München, Germany


© Björn Schuller et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.