Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    A hybrid design of beamformers for voice control devices

    Access Status
    Fulltext not available
    Authors
    Yiu, K.
    Chan, Kit Yan
    Grbić, N.
    Nordholm, Sven
    Date
    2012
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Yiu, K.F.C. and Chan, K.Y. and Grbić, N. and Nordholm, S. 2012. A hybrid design of beamformers for voice control devices. Pacific Journal of Optimization. 8 (3): pp. 533-544.
    Source Title
    Pacific Journal of Optimization
    ISSN
    1348-9151
    URI
    http://hdl.handle.net/20.500.11937/34974
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, a new approach to designing beamformers for voice control device is proposed. It is well-known that under a strong near-field noise with low signal-to-noise ratios (SNR), the performance of speech recognition is deteriorated significantly. However, designing the beamformer for enhancing speech recognition is a slow process and might not be adapted. easily to the changing noise environment. In order to lower the complexity of the design, we intend to exploit the combination of existing optimal beamformer designs, which can be implemented in parallel. These include the least-squares technique and the signal-to-noise ratio maximization technique. We show here that for a given pre-trained speech recognizer and for a finite set of speech commands, neither method has a satisfactory performance in speech recognition accuracy under very low SNRs. However, since the two techniques have different characteristics in speech distortion and noise suppression, we show that it is possible to enhance the speech recognition accuracy by combining these two optimal designs.

    Related items

    Showing items related by title, author, creator and subject.

    • Speech recognition enhancement using beamforming and a genetic algorithm
      Chan, Kit Yan; Yiu, Ka Fai; Low, Siow; Nordholm, Sven; Ling, S. (2009)
      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 ...
    • Speech Enhancement Strategy for Speech Recognition Microcontroller under Noisy Environments
      Chan, Kit Yan; Nordholm, Sven; Yiu, Ka Fai; Togneri, R. (2013)
      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 ...
    • A hybrid noise suppression filter for accuracy enhancement of commercial speech recognizers in varying noisy conditions
      Chan, Kit Yan; Yong, P.; Nordholm, Sven; Yiu, C.; Lam, H. (2014)
      Commercial speech recognizers have made possible many speech control applications such as wheelchair, tone-phone, multifunctional robotic arms and remote controls, for the disabled and paraplegic. However, they have a ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.