F0 modeling for isarn speech synthesis using deep neural networks and syllable-level feature representation
Joint Authors
Janyoi, Pongsathon
Seresangtakul, Pusadee
Source
The International Arab Journal of Information Technology
Issue
Vol. 17, Issue 6 (30 Nov. 2020), pp.906-915, 10 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2020-11-30
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
The generation of the fundamental frequency (F0) plays an important role in speech synthesis, which directly influences the naturalness of synthetic speech.
In conventional parametric speech synthesis, F0 is predicted frame-by-frame.
This method is insufficient to represent F0 contours in larger units, especially tone contours of syllables in tonal languages that deviate as a result of long-term context dependency.
This work proposes a syllable-level F0 model that represents F0 contours within syllables, using syllable-level F0 parameters that comprise the sampling F0 points and dynamic features.
A Deep Neural Network (DNN) was used to represent the relationships between syllable-level contextual features and syllable-level F0 parameters.
The proposed model was examined using an Isarn speech synthesis system with both large and small training sets.
For all training sets, the results of objective and subjective tests indicate that the proposed approach outperforms the baseline systems based on hidden Markov models and DNNS that predict F0 values at the frame level.
American Psychological Association (APA)
Janyoi, Pongsathon& Seresangtakul, Pusadee. 2020. F0 modeling for isarn speech synthesis using deep neural networks and syllable-level feature representation. The International Arab Journal of Information Technology،Vol. 17, no. 6, pp.906-915.
https://search.emarefa.net/detail/BIM-1433876
Modern Language Association (MLA)
Janyoi, Pongsathon& Seresangtakul, Pusadee. F0 modeling for isarn speech synthesis using deep neural networks and syllable-level feature representation. The International Arab Journal of Information Technology Vol. 17, no. 6 (Nov. 2020), pp.906-915.
https://search.emarefa.net/detail/BIM-1433876
American Medical Association (AMA)
Janyoi, Pongsathon& Seresangtakul, Pusadee. F0 modeling for isarn speech synthesis using deep neural networks and syllable-level feature representation. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 6, pp.906-915.
https://search.emarefa.net/detail/BIM-1433876
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 913-914
Record ID
BIM-1433876