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Journal Article An Effective Style Token Weight Control Technique for End-to-End Emotional Speech Synthesis
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Authors
권오성, Jang Inseon, Ahn Chung Hyun, 강홍구
Issue Date
201909
Source
IEEE Signal Processing Letters, v.26 no.9, pp.1383-1387
ISSN
1070-9908
Publisher
IEEE
DOI
https://dx.doi.org/10.1109/LSP.2019.2931673
Project Code
19HR4400, Development of emotional expression service to support hearing/visually impaired, Ahn Chung Hyun
Abstract
In this letter, we propose a high-quality emotional speech synthesis system, using emotional vector space, i.e., the weighted sum of global style tokens (GSTs). Our previous research verified the feasibility of GST-based emotional speech synthesis in an end-to-end text-to-speech synthesis framework. However, selecting appropriate reference audio (RA) signals to extract emotion embedding vectors to the specific types of target emotions remains problematic. To ameliorate the selection problem, we propose an effective way of generating emotion embedding vectors by utilizing the trained GSTs. By assuming that the trained GSTs represent an emotional vector space, we first investigate the distribution of all the training samples depending on the type of each emotion. We then regard the centroid of the distribution as an emotion-specific weighting value, which effectively controls the expressiveness of synthesized speech, even without using the RA for guidance, as it did before. Finally, we confirm that the proposed controlled weight-based method is superior to the conventional emotion label-based methods in terms of perceptual quality and emotion classification accuracy.
KSP Keywords
Control technique, Emotion classification, End to End(E2E), High-quality, Perceptual Quality, Selection problem, Synthesized speech, Text-To-Speech(TTS), Text-To-Speech synthesis, Training samples, Weight control