Speech Synthesis with Mixed Emotions

Article

Zhou, Kun, Sisman, Berrak, Rana, R., Schuller, Bjorn W. and Li, Haizhou. 2023. "Speech Synthesis with Mixed Emotions." IEEE Transactions on Affective Computing. 14 (4), pp. 3120-3134. https://doi.org/10.1109/TAFFC.2022.3233324
Article Title

Speech Synthesis with Mixed Emotions

ERA Journal ID200608
Article CategoryArticle
AuthorsZhou, Kun, Sisman, Berrak, Rana, R., Schuller, Bjorn W. and Li, Haizhou
Journal TitleIEEE Transactions on Affective Computing
Journal Citation14 (4), pp. 3120-3134
Number of Pages15
Year2023
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1949-3045
Digital Object Identifier (DOI)https://doi.org/10.1109/TAFFC.2022.3233324
Web Address (URL)https://ieeexplore.ieee.org/document/10003644
Abstract

Emotional speech synthesis aims to synthesize human voices with various emotional effects. The current studies are mostly focused on imitating an averaged style belonging to a specific emotion type. In this paper, we seek to generate speech with a mixture of emotions at run-time. We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. During the training, the framework does not only explicitly characterize emotion styles but also explores the ordinal nature of emotions by quantifying the differences with other emotions. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector. The objective and subjective evaluations have validated the effectiveness of the proposed framework. To our best knowledge, this research is the first study on modelling, synthesizing, and evaluating mixed emotions in speech.

KeywordsSpeech synthesis; Wheels; Hidden Markov models; Training; Psychology; Emotion recognition; Electronic mail
ANZSRC Field of Research 2020460299. Artificial intelligence not elsewhere classified
Byline AffiliationsNational University of Singapore
University of Texas at Dallas, United States
University of Southern Queensland
Imperial College London, United Kingdom
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