Counterpropagation network and time-frequency shift-tolerant preprocessing for phoneme recognition

Li minn Ang, Nin Cheung Hon

    Research output: Book chapter/Published conference paperConference paperpeer-review

    Abstract

    In this paper, we present an approach using the combination of artificial neural networks and time-frequency distributions to the problem of phoneme recognition in speech processing. For the inputs to the neural network, a two-dimensional Fourier transform is performed on the time-frequency distributions of the speech signals so that the resulting time-frequency pattern of a particular phoneme is always located in the same position regardless of any time and frequency shifts in the speech signal. The implementation of this approach using FFT and CPN is carried out and some preliminary results on the recognition of isolated phonemes are reported.

    Original languageEnglish
    Title of host publicationProceedings of the 1995 IEEE International Conference on Neural Networks
    Pages2037-2040
    Number of pages4
    Publication statusPublished - 01 Dec 1995
    EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust
    Duration: 27 Nov 199501 Dec 1995

    Conference

    ConferenceProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
    CityPerth, Aust
    Period27/11/9501/12/95

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