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 EMIME |
| Authors | |
| Crossref by |
Liang_Idiap-RR-05-2010 |
| Added by: | [UNK] |
| Total mark: | 0 |
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