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    Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments

    192025_192025.pdf (438.9Kb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Nordholm, Sven
    Yiu, Ka Fai
    Togneri, R.
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, Kit Yan and Nordholm, Sven and Yiu, Ka Fai Cedric and Togneri, Roberto. 2013. Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments. Neurocomputing. 118: pp. 279-288.
    Source Title
    Neurocomputing
    DOI
    10.1016/j.neucom.2013.03.008
    ISSN
    0925-2312
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, Volume 118, October 2013, Pages 279-288. http://dx.doi.org/10.1016/j.neucom.2013.03.008

    URI
    http://hdl.handle.net/20.500.11937/26471
    Collection
    • Curtin Research Publications
    Abstract

    Industrial automation with speech control functions is generally installed with a speech recognition sensor which is used as an interface for users to articulate speech commands. However, recognition errors are likely to be produced when background noise surrounds the command spoken into the speech recognition microcontrollers. In this paper, a speech enhancement strategy is proposed to develop noise suppression filters in order to improve the accuracy of speech recognition microcontrollers. It uses a universal estimator, namely a neural network, to enhance the recognition accuracy of microcontrollers by integrating better signals processed by various noise suppression filters, where a global optimization algorithm, namely an intelligent particle swarm optimization, is used to optimize the inbuilt parameters of the neural network in order to maximize accuracy of speech recognition microcontrollers working within noisy environments. The proposed approach overcomes the limitations of the existing noise suppression filters intended to improve recognition accuracy. The performance of the proposed approach was evaluated by a speech recognition microcontroller, which is used in electronic products with speech control functions. Results show that the accuracy of the speech recognition microcontroller can be improved using the proposed approach, when working under low signal to noise ratio conditions in the industrial environments of automobile engines and factory machines.

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