Personalising speech-to-speech translation: Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis

Personalising speech-to-speech translation: Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis” by J. Dines, H. Liang, L. Saheer, M. Gibson, W. Byrne, K. Oura, K. Tokuda, J. Yamagishi, S. King, M. Wester, T. Hirsimäki, R. Karhila, and M. Kurimo. Computer Speech and Language, 2011. Special Issue on Speech Translation. Published online 17 September 2011.

Abstract

In this paper we present results of unsupervised cross-lingual speaker adaptation applied to text-to-speech synthesis. The application of our research is the personalisation of speech-to-speech translation in which we employ a HMM statistical framework for both speech recognition and synthesis. This framework provides a logical mechanism to adapt synthesised speech output to the voice of the user by way of speech recognition. In this work we present results of several different unsupervised and cross-lingual adaptation approaches as well as an end-to-end speaker adaptive speech-to-speech translation system. Our experiments show that we can successfully apply speaker adaptation in both unsupervised and cross-lingual scenarios and our proposed algorithms seem to generalise well for several language pairs. We also discuss important future directions including the need for better evaluation metrics.

BibTeX entry:

@article{csl11,
   author = {J. Dines and H. Liang and L. Saheer and M. Gibson and W.
	Byrne and K. Oura and K. Tokuda and J. Yamagishi and S. King and
	M. Wester and T. Hirsim{\"a}ki and R. Karhila and M. Kurimo},
   title = {Personalising speech-to-speech translation: Unsupervised
	cross-lingual speaker adaptation for {HMM}-based speech
	synthesis},
   journal = {Computer Speech and Language},
   pages = {(18 pages)},
   year = {2011},
   note = {Special Issue on Speech Translation. Published online 17
	September 2011.},
   url = {http://www.sciencedirect.com/science/article/pii/S0885230811000441}
}

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