• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Noise-robust linear prediction cepstral features for network speech recognition

    Thumbnail
    View/Open
    AlatwiPUB2612.pdf (460.9Kb)
    Author
    Alatwi, Aadel
    So, Stephen
    Paliwal, Kuldip
    Year published
    2016
    Metadata
    Show full item record
    Abstract
    In this paper, we propose a perceptually-motivated method for modifying the speech power spectrum to obtain a set of linear prediction coding (LPC) parameters that possess good noiserobustness properties in network speech recognition. Speech recognition experiments were performed to compare the accuracy obtained from MFCC features extracted from AMR-coded speech that use these modified LPC parameters, as well as from LPCCs extracted from AMR bitstream parameters. The results show that when using the proposed LP analysis method, the recognition performance was on average 1.2% - 6.1% better than when using the ...
    View more >
    In this paper, we propose a perceptually-motivated method for modifying the speech power spectrum to obtain a set of linear prediction coding (LPC) parameters that possess good noiserobustness properties in network speech recognition. Speech recognition experiments were performed to compare the accuracy obtained from MFCC features extracted from AMR-coded speech that use these modified LPC parameters, as well as from LPCCs extracted from AMR bitstream parameters. The results show that when using the proposed LP analysis method, the recognition performance was on average 1.2% - 6.1% better than when using the conventional LP method, depending on the recognition task.
    View less >
    Conference Title
    Proceedings of the Sixteenth Australasian International Conference on Speech Science and Technology
    Publisher URI
    http://www.assta.org/?q=sst-conferences
    Copyright Statement
    © 2016 ASSTA. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Subject
    Signal Processing
    Publication URI
    http://hdl.handle.net/10072/100785
    Collection
    • Conference outputs

    Footer

    Social media

    • Facebook
    • Twitter
    • YouTube
    • Instagram
    • Linkedin
    First peoples of Australia
    • Aboriginal
    • Torres Strait Islander

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane
    • Australia