DocumentCode :
1150019
Title :
TechWare: HMM-based speech synthesis resources [Best of the Web]
Author :
Zen, Heiga ; Tokuda, Keiichi
Author_Institution :
Speech Technol. Group, Toshiba Res. Eur. Ltd., Cambridge, UK
Volume :
26
Issue :
4
fYear :
2009
fDate :
7/1/2009 12:00:00 AM
Firstpage :
95
Lastpage :
97
Abstract :
This paper focuses on hidden Markov model (HMM)- based speech synthesis, which has recently been demonstrated to be very effective in generating high-quality speech and started dominating speech synthesis research. The attractive point of this approach is that the synthesized speech can easily be modified by transforming HMM parameters with a small amount of speech data. Thus it is very useful for constructing speech synthesizers with various voice characteristics, speaking styles, and emotions.
Keywords :
hidden Markov models; speech synthesis; HMM-based speech synthesis; emotion; hidden Markov model; speaking style; speech data; voice characteristics; Databases; Degradation; Hidden Markov models; History; Interpolation; Maximum likelihood estimation; Parameter estimation; Speech synthesis; Synthesizers; Wikipedia;
fLanguage :
English
Journal_Title :
Signal Processing Magazine, IEEE
Publisher :
ieee
ISSN :
1053-5888
Type :
jour
DOI :
10.1109/MSP.2009.932563
Filename :
5174504
Link To Document :
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