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- W2188836405 abstract "Thispaper presents astatistical method forspeaker feature extraction andvoice conversion within Sinusoidal+Noise (S+N)modeling framework. Withfundamental researches onspeaker characteristics embedded intheparameter sets ofS+Nmodel, wefound thevector sets ofStatistical EigenVoice(SEV)andWeighted Statistical EigenVoice (wSEV), whicharebasis vectors ofGMM representation, havesignificant properties: approximately speaker-dependent and language-independent. Piered bythefeature vectors ofSEV andwSEV,we address anewalgorithm forcontext-free voice conversion. Subjective tests suggest that theSEVbasedmethodachieves convincing results while maintaininghighsynthesis quality incomparison tothetraditional LPCapproaches." @default.
- W2188836405 created "2016-06-24" @default.
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- W2188836405 date "2005-01-01" @default.
- W2188836405 modified "2023-09-27" @default.
- W2188836405 title "STATISTICALEIGENVOICE:SPEAKERFEATURESWITHINS+NFRAMEWORK AND A WAY TOWARDS LANGUAGE-INDEPENDENTVOICECONVERSION" @default.
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