Fine-Grained Quantitative Emotion Editing for Speech Generation
arxiv(2024)
摘要
It remains a significant challenge how to quantitatively control the
expressiveness of speech emotion in speech generation. In this work, we present
a novel approach for manipulating the rendering of emotions for speech
generation. We propose a hierarchical emotion distribution extractor, i.e.
Hierarchical ED, that quantifies the intensity of emotions at different levels
of granularity. Support vector machines (SVMs) are employed to rank emotion
intensity, resulting in a hierarchical emotional embedding. Hierarchical ED is
subsequently integrated into the FastSpeech2 framework, guiding the model to
learn emotion intensity at phoneme, word, and utterance levels. During
synthesis, users can manually edit the emotional intensity of the generated
voices. Both objective and subjective evaluations demonstrate the effectiveness
of the proposed network in terms of fine-grained quantitative emotion editing.
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