rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Malay speech intelligibility test for deaf children
    sub title: phoneme recognition using artificial neural network
  name:
    name part: Zulkhairi Mohd. Yusof
    role:
      role term: creator
  name:
    name part: International Islamic University Malaysia
    name part: Kulliyyah of Information Communication and Technology
  genre: Theses, IIUM local
  origin info:
    place:
      place term: my
    place:
      place term: Kuala Lumpur
    publisher: Kulliyyah of Information Communication and Technology, International Islamic University Malaysia
    date issued: 2011
  language:
    language term: eng
  physical description:
    extent: xx, 227 leaves : ill. ; 30cm.
  abstract: It is estimated that about 2000 deaf children are born each year in Malaysia. Most deaf Malaysian children have very poor speech intelligibility. Reduced intelligibility severely compromises communication and social interaction for affected individuals. Although speech deficiencies in the deaf are quite difficult to overcome, learning to produce intelligible speech is not an impossible task. Studies have shown that deaf children receiving Cued Speech can acquire reasonable speech intelligibility, surpassing the majority of signing children in verbal language skills. A reliable measure of speech intelligibility for deaf children is required for several reasons: to provide an index of the severity of speech disorder, to assist in treatment decisions, and to quantify changes which may result from intervention or treatment. This thesis investigates the approach to measure speech intelligibility of deaf Malaysian children. The research discussed in this work starts with the experiments on a practical Malay Speech Intelligibility Test (MSIT), suitable for use within deaf Malaysian children training programme. In this study, speech intelligibility of deaf children is measured through the ability to say simple nonsense syllables (consisting of a consonant and a vowel) for all 22 Malay consonants. The MSIT score should indicate how well these children can produce speech; the higher the score, the better their speech intelligibility. The next course of action was to investigate phoneme recognition system that will suit MSIT. Artificial neural network was utilized to effectively model the distribution of feature vectors present in speech signals for classification. A novel approach using speech spectrum image becomes the inputs to a three-layer MLP (Multi-layer Perceptron) neural network. The input feature sets for the intelligent phoneme identification were based on the intrinsic characteristics of Malay syllables shown in the captured speech signal spectrum image. The spectr…
  note: by Zulkhairi Mohd. Yusof
  note: Abstract in English and Arabic.
  note: "A thesis submitted in fulfilment of the requirement for the degree of Doctor of Philosophy in Management information System.
  note: Thesis (Ph.D)--International Islamic University Malaysia, 2011.
  note: Includes bibliographical references (leaves 199-208).
  subject:
    geographic code: a-my---
  subject:
    topic: Speech
    topic: Intelligibility of
  subject:
    topic: Deafness in children
  classification: TK5981
  identifier: http://studentrepo.iium.edu.my/handle/123456789/5463
  record info:
    record content source: UIAM
    record creation date: 110614
    language of cataloging:
      language term: eng