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