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

Li minn Ang, Nin Cheung Hon

Research output: Book chapter/Published conference paperConference paper

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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