scholarly journals Improvement in corpus-based generation of F0 contours using generation process model for emotional speech synthesis

2004 ◽  
Author(s):  
Keikichi Hirose
Keyword(s):  
Speech Synthesis ◽  
Process Model ◽  
Generation Process ◽  
Emotional Speech

Synthesis of F0 contours using generation process model parameters predicted from unlabeled corpora: application to emotional speech synthesis

2005 ◽  
Vol 46 (3-4) ◽  
pp. 385-404 ◽  
Author(s):  
Keikichi Hirose ◽  
Kentaro Sato ◽  
Yasufumi Asano ◽  
Nobuaki Minematsu
Keyword(s):  
Speech Synthesis ◽  
Process Model ◽  
Model Parameters ◽  
Generation Process ◽  
Emotional Speech

Improved generation of fundamental frequency in HMM-based speech synthesis using generation process model

2010 ◽  
Author(s):  
Miaomiao Wang ◽  
Miaomiao Wen ◽  
Keikichi Hirose ◽  
Nobuaki Minematsu
Keyword(s):  
Fundamental Frequency ◽  
Speech Synthesis ◽  
Process Model ◽  
Generation Process

Adaptation of prosody in speech synthesis by changing command values of the generation process model of fundamental frequency

2011 ◽  
Author(s):  
Keikichi Hirose ◽  
Keiko Ochi ◽  
Ryusuke Mihara ◽  
Hiroya Hashimoto ◽  
Daisuke Saito ◽  
...  
Keyword(s):  
Fundamental Frequency ◽  
Speech Synthesis ◽  
Process Model ◽  
Generation Process

Voice and Speech Synthesis—Highlighting the Control of Prosody

2018 ◽  
pp. 756-776
Author(s):  
Keikichi Hirose
Keyword(s):  
Speech Synthesis ◽  
Process Model ◽  
Voice Conversion ◽  
Synthetic Speech ◽  
Synthesis Process ◽  
Generation Process ◽  
Sound Generation ◽  
Prosodic Features ◽  
Speech Corpus ◽  
Parametric Speech Synthesis

After starting as an effort to mimic the human process of speech sound generation, the quality of synthetic speech has reached a level that makes it difficult to notice that it is synthetic. This owes to the development of waveform concatenation methods which select the most appropriate speech segments from a huge speech corpus. Although the lack of flexibility in producing various speech qualities/styles has been pointed out, this problem is about to be solved by introducing statistical frameworks into parametric speech synthesis. Now, a speaker can even speak a foreign language in his/her voice using advanced voice-conversion techniques. However, if we consider prosodic features of speech, current technologies are not appropriate to handle their hierarchical structure over a long time span. Introduction of prosody modelling into the speech-synthesis process is necessary. In this chapter, after viewing the history of voice/speech synthesis, technologies are explained, starting from text-to-speech and concept-to-speech conversion. Then, methods of sound generation are introduced. Statistical parametric speech synthesis, especially HMM-based speech synthesis, is introduced as a technology that enables flexible speech synthesis—that is, synthetic speech with various qualities/styles requiring a smaller amount of speech corpus. After that, the problem of frame-by-frame processing for prosodic features is addressed and the importance of prosody modelling is pointed out. Prosodic (fundamental frequency) modelling is surveyed and, finally, the generation process model is introduced with some experimental results when applied to HMM-based speech synthesis.

Sign in / Sign up

Export Citation Format

Share Document