Báo cáo hóa học: "Research Article Recognition of Noisy Speech: A Comparative Survey of Robust Model Architecture and Feature Enhancement"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Recognition of Noisy Speech: A Comparative Survey of Robust Model Architecture and Feature Enhancement | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2009 Article ID 942617 17 pages doi 2009 942617 Research Article Recognition of Noisy Speech A Comparative Survey of Robust Model Architecture and Feature Enhancement Bjorn Schuller 1 Martin Wollmer 1 Tobias Moosmayr 2 and Gerhard Rigoll1 1Institute for Human-Machine Communication Technische Universitat Munchen TUM 80290 Munich Germany 2BMW Group Forschungs- und Innovationszentrum Akustik Komfort und Werterhaltung 80788 Munchen Germany Correspondence should be addressed to Bjorn Schuller schuller@ Received 28 October 2008 Revised 21 January 2009 Accepted 15 February 2009 Recommended by Li Deng 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 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. Copyright 2009 Bjorn Schuller et al. 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. 1. Introduction The

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