Speech synthesis of phonemic triplets through a neural network-controlled formant synthesizer

Michael S. Scordilis, John N. Gowdy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Summary form only given. The problems associated with automatic speech synthesis are related, to a large extent, to the methods of controlling the mathematical models of the human vocal tract and its properties as they change with time during discourse. In formant synthesis, which is the most effective synthesis method, rules are applied to relate the incoming phonemic information to values of the synthesizer control vectors. Such rules are usually developed through the analysis of a representative set of utterances and adjusted with listening tests. The tedious nature of the parameter extraction process and the lack of unambiguous relationships of acoustic events with spectral information have hindered the effective control of the models. In the present work, artificial neural networks were employed to assist with the latter concern. For this purpose, 56 common words comprising larynx-produced phonemes were analyzed and used to train a network cluster. The system was able to produce intelligible speech for certain phonemic combinations.

Original languageEnglish (US)
Title of host publicationProceedings. IJCNN - International Joint Conference on Neural Networks
Editors Anon
PublisherPubl by IEEE
Number of pages1
ISBN (Print)0780301641
StatePublished - Jan 1 1992
EventInternational Joint Conference on Neural Networks - IJCNN-91-Seattle - Seattle, WA, USA
Duration: Jul 8 1991Jul 12 1991

Publication series

NameProceedings. IJCNN - International Joint Conference on Neural Networks

Other

OtherInternational Joint Conference on Neural Networks - IJCNN-91-Seattle
CitySeattle, WA, USA
Period7/8/917/12/91

ASJC Scopus subject areas

  • Engineering(all)

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    Scordilis, M. S., & Gowdy, J. N. (1992). Speech synthesis of phonemic triplets through a neural network-controlled formant synthesizer. In Anon (Ed.), Proceedings. IJCNN - International Joint Conference on Neural Networks (Proceedings. IJCNN - International Joint Conference on Neural Networks). Publ by IEEE.