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Publication
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| Title of Article |
Tonal Contour Generation for Isarn Speech Synthesis Using Deep Learning and Sampling-Based F0 Representation |
| Date of Acceptance |
21 September 2020 |
| Journal |
| Title of Journal |
Applied Sciences |
| Standard |
ISI |
| Institute of Journal |
MDPI (Basel, Switzerland) |
| ISBN/ISSN |
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| Volume |
10 |
| Issue |
18 |
| Month |
September |
| Year of Publication |
2020 |
| Page |
1-18 |
| Abstract |
The modeling of fundamental frequency (F0) in speech synthesis is a critical factor affecting the intelligibility and naturalness of synthesized speech. In this paper, we focus on improving the modeling of F0 for Isarn speech synthesis. We propose the F0 model for this based on a recurrent neural network (RNN). Sampled values of F0 are used at the syllable level of continuous Isarn speech combined with their dynamic features to represent supra-segmental properties of the F0 contour. Different architectures of the deep RNNs and different combinations of linguistic features are analyzed to obtain conditions for the best performance. To assess the proposed method, we compared it with several RNN-based baselines. The results of objective and subjective tests indicate that the proposed model significantly outperformed the baseline RNN model that predicts values of F0 at the frame level, and the baseline RNN model that represents the F0 contours of syllables by using discrete cosine transform. |
| Keyword |
tone, fundamental frequency, recurrent neural networks, Isarn dialect, speech synthesis |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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