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Statistical eigenvoice: speaker features within S+N framework and a way towards language-independent voice conversion

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2 Author(s)
Feng Huang ; Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China ; Junxun Yin

This paper presents a statistical method for speaker feature extraction and voice conversion within sinusoidal + noise (S+N) modeling framework. With fundamental researches on speaker characteristics embedded in the parameter sets of S+N model, we found the vector sets of statistical eigenvoice (SEV) and weighted statistical eigenvoice (wSEV), which are basis vectors of GMM representation, have significant properties: approximately speaker-dependent and language-independent. Piered by the feature vectors of SEV and wSEV, we address a new algorithm for context-free voice conversion. Subjective tests suggest that the SEV-based method achieves convincing results while maintaining high synthesis quality in comparison to the traditional LPC approaches.

Published in:

Intelligent Signal Processing and Communication Systems, 2005. ISPACS 2005. Proceedings of 2005 International Symposium on

Date of Conference:

13-16 Dec. 2005