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Two-level recognition of isolated word using neural nets

Howard, IS; Huckvale, MA; (2002) Two-level recognition of isolated word using neural nets. In: 1989 First IEE International Conference on Artificial Neural Networks, (Conf. Publ. No. 313). (pp. pp. 90-94). IET: London, UK,. Green open access

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Abstract

Describes a neural-net based isolated word recogniser that has a better performance on a standard multi-speaker database than the reference hidden Markov model recogniser. The complete neural net recogniser is formed from two parts: a front-end which transforms the complex acoustic specification of the speech into a simplified phonetic feature specification, and a whole-word discriminator net. Each level was trained separately, thus considerably reducing the time necessary to train the overall system.

Type: Proceedings paper
Title: Two-level recognition of isolated word using neural nets
Event: 1989 First IEE International Conference on Artificial Neural Networks, (Conf. Publ. No. 313)
Location: LONDON, ENGLAND
Dates: 16 October 1989 - 18 October 1989
ISBN: 0-85296-388-2
Open access status: An open access version is available from UCL Discovery
Publisher version: https://ieeexplore.ieee.org/document/51937/similar...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Neural networks, Speech recognition
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Speech, Hearing and Phonetic Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10091731
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