DocumentCode :
2705682
Title :
Statistical Parametric Speech Synthesis
Author :
Black, Alan W. ; Zen, Heiga ; Tokuda, Keiichi
Author_Institution :
Language Technol. Inst., Carnegie Mellon Univ., Pittsburgh, PA
Volume :
4
fYear :
2007
fDate :
15-20 April 2007
Abstract :
This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.
Keywords :
hidden Markov models; speech synthesis; HMM-based generation synthesis; statistical parametric speech synthesis; unit selection technology; Computer science; Costs; Databases; Degradation; Hidden Markov models; Loudspeakers; Natural languages; Speech synthesis; Synthesizers; Testing; Speech synthesis; hidden Markov models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Conference_Location :
Honolulu, HI
ISSN :
1520-6149
Print_ISBN :
1-4244-0727-3
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2007.367298
Filename :
4218329
Link To Document :
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