• DocumentCode
    1661722
  • Title

    Squad-based expert modules for closing diphthong recognition

  • Author

    Kirkland, John

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Canterbury Univ., Christchurch, New Zealand
  • fYear
    1995
  • Firstpage
    302
  • Lastpage
    305
  • Abstract
    The paper presents a new method of forming expert modules for modular time delay neural networks (modular TDNNs) using ensembles of similarly trained TDNNs referred to as squads. Squad base expert modules for closing diphthong recognition are compared with traditional expert modules comprising individual TDNNs and are found to afford significantly better false positive error performances, while recognition performances are comparable or better. Traditional and squad based expert modules formed from three different TDNN variants are compared. One of these variants, sequence token TDNN, embodies a novel method of using traditional TDNNs for closing diphthong recognition and is found to outperform the other variants when squad based expert modules are used
  • Keywords
    expert systems; natural languages; neural nets; speech recognition; TDNN variants; automated speech recognition; closing diphthong recognition; false positive error performances; modular TDNNs; modular time delay neural networks; phoneme realizations; recognition performance; sequence token TDNN; squad based expert modules; Automatic speech recognition; Frequency domain analysis; Natural languages; Neural networks; Speech recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on
  • Conference_Location
    Dunedin
  • Print_ISBN
    0-8186-7174-2
  • Type

    conf

  • DOI
    10.1109/ANNES.1995.499494
  • Filename
    499494