000192612 001__ 192612
000192612 005__ 20190812205733.0
000192612 037__ $$aCONF
000192612 269__ $$a2012
000192612 260__ $$bIEEE SPS (ICASSP)$$c2012
000192612 336__ $$aConference Papers
000192612 520__ $$aRecent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR based adaptation techniques, being much closer in quality to that generated by the original average voice model. However with only a single parameter, VTLN captures very few speaker specific characteristics when compared to linear transform based adaptation techniques. This paper proposes that the merits of VTLN can be combined with those of linear transform based adaptation in a hierarchial Bayesian framework, where VTLN is used as the prior information. A novel technique for propagating the gender information from the VTLN prior through constrained structural maximum aposteriori linear regression (CSMAPLR) adaptation is presented. Experiments show that the resulting transformation has improved speech quality with better naturalness, intelligibility and improved speaker similarity.
000192612 6531_ $$aconstrained structural maximum a posteriori linear regression
000192612 6531_ $$ahidden Markov models
000192612 6531_ $$aspeaker adaptation
000192612 6531_ $$aStatistical parametric speech synthesis
000192612 6531_ $$avocal tract length normalization
000192612 700__ $$aSaheer, Lakshmi
000192612 700__ $$aYamagishi, Junichi
000192612 700__ $$aGarner, Philip N.
000192612 700__ $$g192380$$aDines, John$$0243992
000192612 7112_ $$cKyoto, Japan$$aProceedings in International conference on Speech and Signal processing
000192612 8564_ $$zRelated documents$$uhttp://publications.idiap.ch/index.php/publications/showcite/Saheer_Idiap-RR-11-2012
000192612 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/192612/files/Saheer_ICASSP_2012.pdf$$s150349
000192612 909C0 $$xU10381$$pLIDIAP$$0252189
000192612 909CO $$ooai:infoscience.tind.io:192612$$qGLOBAL_SET$$pconf$$pSTI
000192612 937__ $$aEPFL-CONF-192612
000192612 970__ $$aSaheer_ICASSP_2012/LIDIAP
000192612 973__ $$aEPFL
000192612 980__ $$aCONF