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A Novel Method for Speech Data Mining

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Author(s): Sheng-Hui Dai | Gang-Yong lin | Hua-Qing Zhou

Journal: Journal of Software
ISSN 1796-217X

Volume: 6;
Issue: 1;
Start page: 64;
Date: 2011;
Original page

Keywords: Classifiers | clustering | data reduction | relevance feedback | speech data mining

ABSTRACT
Text-to-Speech (TTS) system is one to translate given text to speech which can be used in various applications such as information releasing systems, voice response devices, voice services in E-mail and reading machines for the blind. Great progress has been made in the research on Chinese TTS systems and several Chinese TTS systems have been published. However, because of the complexity of Chinese, the current available speech patterns are not very fine. The speech quality of those systems developed from these patterns is not good enough to meet the needs of users. The main purpose of this paper is to gain a refined prosodic model of Chinese speech. Traditional methods are not used in this thesis and data mining techniques are employed. Data mining is the process of discovering advantageous patterns in database. There are now many data mining algorithms, one of which is neural network. This paper presents a data mining system using clustering algorithm to find useful patterns from Chinese speech database. Study on the tone changes of Chinese two- word phrases has been made and good results have been achieved. They are helpful to develop high quality Chinese TTS systems.

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