Emotion Intensity and its Control for Emotional Voice Conversion

Article

Zhou, Kun, Sisman, Berrak, Rana, Rajib, Schuller, Bjorn W. and Li, Haizhou. 2023. "Emotion Intensity and its Control for Emotional Voice Conversion." IEEE Transactions on Affective Computing. 14 (1), pp. 31-48. https://doi.org/10.1109/TAFFC.2022.3175578
Article Title

Emotion Intensity and its Control for Emotional Voice Conversion

ERA Journal ID200608
Article CategoryArticle
AuthorsZhou, Kun (Author), Sisman, Berrak (Author), Rana, Rajib (Author), Schuller, Bjorn W. (Author) and Li, Haizhou (Author)
Journal TitleIEEE Transactions on Affective Computing
Journal Citation14 (1), pp. 31-48
Number of Pages18
Year2023
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationUnited States
ISSN1949-3045
Digital Object Identifier (DOI)https://doi.org/10.1109/TAFFC.2022.3175578
Web Address (URL)https://ieeexplore.ieee.org/document/9778970
Abstract

Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In EVC, emotions are usually treated as discrete categories overlooking the fact that speech also conveys emotions with various intensity levels that the listener can perceive. In this work, we aim to explicitly characterize and control the intensity of emotion. We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding. We further learn the actual emotion encoder from an emotion-labelled database and study the use of relative attributes to represent fine-grained emotion intensity. To ensure emotional intelligibility, we incorporate emotion classification loss and emotion embedding similarity loss into the training of the EVC network. As desired, the proposed network controls the fine-grained emotion intensity in the output speech. Through both objective and subjective evaluations, we validate the effectiveness of the proposed network for emotional expressiveness and emotion intensity control.

KeywordsEmotional voice conversion, emotion intensity, sequence-to-sequence, perceptual loss, limited data, relative attribute
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020460211. Speech production
461103. Deep learning
Byline AffiliationsNational University of Singapore
Singapore University of Technology and Design
School of Mathematics, Physics and Computing
Imperial College London, United Kingdom
Institution of OriginUniversity of Southern Queensland
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