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    Speech recognition enhancement using beamforming and a genetic algorithm

    134902_134902.pdf (373.5Kb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Yiu, Ka Fai
    Low, Siow
    Nordholm, Sven
    Ling, S.
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Chan, Kit and Yiu, Ka Fai and Low, Siow and Nordholm, Sven and Ling, S. 2009. Speech recognition enhancement using beamforming and a genetic algorithm, in Zhou, W. (ed), 2nd IEEE International Workshop on Data Mining and Artificial Intelligence (DMAI 2009) with Third International Conference on Network and System Security (NSS 2009), Oct 19 2009, pp. 510-515.Gold Coast, Australia: IEEE Computer Society.
    Source Title
    Proceedings of the 2nd IEEE international workshop on data mining and artificial intelligence (DMAI 2009) with third international conference on network and system security (NSS 2009)
    Source Conference
    2nd IEEE International Workshop on Data Mining and Artificial Intelligence (DMAI 2009) with Third International Conference on Network and System Security (NSS 2009)
    Additional URLs
    http://doi.ieeecomputersociety.org/10.1109/NSS.2009.44
    ISBN
    9780769538389
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    Copyright © 2009 IEEE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/37617
    Collection
    • Curtin Research Publications
    Abstract

    This paper proposes a genetic algorithm (GA) based beamformer to optimize speech recognition accuracy for a pretrained speech recognizer. The proposed beamformer is designed to tackle the non-differentiable and non-linear natures of speech recognition by employing the GA algorithm to search for the optimal beamformer weights. Specifically, a population of beamformer weights is reproduced by crossover and mutation until the optimal beamformer weights are obtained. Results show that the speech recognition accuracies can be greatly improved even in noisy environments.

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