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: Incorporating Knowledge Sources into Statistical Speech Recognition
  name:
    name part: Minker, Wolfgang.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Nakamura, Satoshi.
    role:
      role term: author.
  name:
    name part: Markov, Konstantin.
    role:
      role term: author.
  name:
    name part: Sakti, Sakriani.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2009
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXIV, 196 p. 100 illus. online resource.
  abstract: Incorporating Knowledge Sources into Statistical Speech Recognition offers solutions for enhancing the robustness of a statistical automatic speech recognition (ASR) system by incorporating various additional knowledge sources while keeping the training and recognition effort feasible. The authors provide an efficient general framework for incorporating knowledge sources into state-of-the-art statistical ASR systems. This framework, which is called GFIKS (graphical framework to incorporate additional knowledge sources), was designed by utilizing the concept of the Bayesian network (BN) framework. This framework allows probabilistic relationships among different information sources to be learned, various kinds of knowledge sources to be incorporated, and a probabilistic function of the model to be formulated. Incorporating Knowledge Sources into Statistical Speech Recognition demonstrates how the statistical speech recognition system may incorporate additional information sources by utilizing GFIKS at different levels of ASR. The incorporation of various knowledge sources, including background noises, accent, gender and wide phonetic knowledge information, in modeling is discussed theoretically and analyzed experimentally.
  table of contents: and Book Overview -- Statistical Speech Recognition -- Graphical Framework to Incorporate Knowledge Sources -- Speech Recognition Using GFIKS -- Conclusions and Future Directions.
  note: by Wolfgang Minker, Satoshi Nakamura, Konstantin Markov, Sakriani Sakti.
  subject:
    topic: Engineering
  subject:
    topic: Computer communication systems
  subject:
    topic: Acoustics
  subject:
    topic: Electrical engineering
  subject:
    topic: Engineering
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Acoustics
  subject:
    topic: Communications Engineering, Networks
  subject:
    topic: Computer Communication Networks
  subject:
    topic: Electrical Engineering
  classification: TK5102.9
  classification: TA1637-1638
  classification: TK7882.S65
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Electrical Engineering, 42
  identifier: 9780387858302
  identifier: http://dx.doi.org/10.1007/978-0-387-85830-2
  record info:
    record creation date: 100301
    record change date: 20151204180938.0
    record identifier: 978-0-387-85830-2