A hybrid neural system for phonematic transformation

Igor T. Podolak; Seong Whan Lee; Andrzej Bielecki; Elzbieta Majkut

A hybrid neural system for phonematic transformation

Igor T. Podolak, Seong Whan Lee, Andrzej Bielecki, Elzbieta Majkut

Department of Brain and Cognitive Engineering

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Text-to-phoneme conversion is a common problem in speech processing. This can be done using a rule-based system or a neural network. In this paper we propose a solution to this problem using a modular hybrid system that uses basic rules to subdivide the original problem into easier tasks which are then solved by dedicated neural networks. Such a solution can be more rapidly constructed, and is easily extendable. A voting committee concept is used to enhance generalization abilities of the system.

Original language	English
Pages (from-to)	957-960
Number of pages	4
Journal	Proceedings - International Conference on Pattern Recognition
Volume	15
Issue number	2
Publication status	Published - 2000

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Cite this

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AU - Podolak, Igor T.

AU - Lee, Seong Whan

AU - Bielecki, Andrzej

AU - Majkut, Elzbieta

N1 - Funding Information: 'This research was supported by Creative Research Initiatives of the Ministry of Science and Technology, Korea.

PY - 2000

Y1 - 2000

N2 - Text-to-phoneme conversion is a common problem in speech processing. This can be done using a rule-based system or a neural network. In this paper we propose a solution to this problem using a modular hybrid system that uses basic rules to subdivide the original problem into easier tasks which are then solved by dedicated neural networks. Such a solution can be more rapidly constructed, and is easily extendable. A voting committee concept is used to enhance generalization abilities of the system.

AB - Text-to-phoneme conversion is a common problem in speech processing. This can be done using a rule-based system or a neural network. In this paper we propose a solution to this problem using a modular hybrid system that uses basic rules to subdivide the original problem into easier tasks which are then solved by dedicated neural networks. Such a solution can be more rapidly constructed, and is easily extendable. A voting committee concept is used to enhance generalization abilities of the system.

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M3 - Article

AN - SCOPUS:10244222152

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ER -

A hybrid neural system for phonematic transformation

Abstract

ASJC Scopus subject areas

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