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A Comparison of Supervised and Unsupervised Cross-Lingual Speaker Adaptation Approaches for HMM-Based Speech Synthesis
Type of publication: Conference paper
Citation: Liang_ICASSP_2010
Booktitle: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing
Year: 2010
Month: 3
Location: Dallas, U.S.A.
Crossref: Liang_Idiap-RR-05-2010:
Abstract: The EMIME project aims to build a personalized speech-to-speech translator, such that spoken input of a user in one language is used to produce spoken output that still sounds like the user's voice however in another language. This distinctiveness makes unsupervised cross-lingual speaker adaptation one key to the project's success. So far, research has been conducted into unsupervised and cross-lingual cases separately by means of decision tree marginalization and HMM state mapping respectively. In this paper we combine the two techniques to perform unsupervised cross-lingual speaker adaptation. The performance of eight speaker adaptation systems (supervised vs. unsupervised, intra-lingual vs. cross-lingual) are compared using objective and subjective evaluations. Experimental results show the performance of unsupervised cross-lingual speaker adaptation is comparable to that of the supervised case in terms of spectrum adaptation in the EMIME scenario, even though automatically obtained transcriptions have a very high phoneme error rate.
Keywords: decision tree marginalization, HMM state mapping, unsupervised cross-lingual speaker adaptation
Projects Idiap
Authors Liang, Hui
Dines, John
Saheer, Lakshmi
Crossref by Liang_Idiap-RR-05-2010
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  • Liang_ICASSP_2010.pdf