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