• 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 Theses
    • Theses - Higher Degree by Research
    • View Item
    • Home
    • Griffith Theses
    • Theses - Higher Degree by Research
    • 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
  • Robust Speech Recognition in Adverse Environments

    Thumbnail
    View/Open
    Lilly_2000_01Thesis.pdf (5.380Mb)
    Author(s)
    Lilly, Brendon Troy
    Primary Supervisor
    Paliwal, Kuldip K.
    Year published
    2000
    Metadata
    Show full item record
    Abstract
    The performance of an automatic speech recognition system degrades drastically when there is a mismatch between training and testing environments. The aim of robust speech recognition is to overcome this mismatch. Numerous methods have been reported in the literature that attempt to provide robustness to this mismatch. This thesis investigates several different techniques at different stages of the recognition process that are suitable for robust speech recognition. All experiments are conducted on the ISOLET database. The TIMIT database was also used to confirm some of the experimental results. A number of speech enhancement ...
    View more >
    The performance of an automatic speech recognition system degrades drastically when there is a mismatch between training and testing environments. The aim of robust speech recognition is to overcome this mismatch. Numerous methods have been reported in the literature that attempt to provide robustness to this mismatch. This thesis investigates several different techniques at different stages of the recognition process that are suitable for robust speech recognition. All experiments are conducted on the ISOLET database. The TIMIT database was also used to confirm some of the experimental results. A number of speech enhancement techniques have been used in the past for speech recognition to achieve robustness with respect to noise. A speech enhancement system attempts to reduce noise from the noisy speech signal and is used as a pre-processor to a speech recogniser. In this thesis, a singular value decomposition (SVD) based speech enhancement method is used for robust speech recognition. The speech recognition performance of the SVD method is compared to that of the popular spectral subtraction method. Speech recognition performance is directly affected by the performance of the feature extraction stage. This thesis provides a comprehensive evaluation of a number of acoustic front-ends for robust speech recognition. It also investigates the use of human auditory properties for robust feature extraction. Two acoustic front-ends based on simultaneous masking and variable frequency and temporal resolutions are proposed and their performance is investigated for speech distorted by additive noise and channel distortion. This thesis also investigates the degradation in speech recognition performance due to speech coding distortion. For this, seven different speech coders operating at different bit rates are simulated and the speech recogniser is utilised through each of these coders. The MAP adaptation technique is then applied to adapt the model parameters to the speech coding environment. The resulting system is found to perform well in the presence of the speech coding distortion.
    View less >
    Thesis Type
    Thesis (PhD Doctorate)
    Degree Program
    Doctor of Philosophy (PhD)
    School
    School of Microelectronic Engineering
    DOI
    https://doi.org/10.25904/1912/2212
    Copyright Statement
    The author owns the copyright in this thesis, unless stated otherwise.
    Subject
    Robust speech recognition
    Singular value decomposition
    Speech enhancement
    Spectral subtraction
    Speech coding distortion
    Publication URI
    http://hdl.handle.net/10072/367109
    Collection
    • Theses - Higher Degree by Research

    Footer

    Disclaimer

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

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander