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Speech emotion conversion is the task of converting the expressed emotion ofa spoken utterance to a target emotion while preserving the lexical content andspeaker identity. While most existing works in speech emotion conversion relyon acted-out datasets and parallel data samples, in this work we specificallyfocus on more challenging in-the-wild scenarios and do not rely on paralleldata. To this end, we propose a diffusion-based generative model for speechemotion conversion, the EmoConv-Diff, that is trained to reconstruct an inpututterance while also conditioning on its emotion. Subsequently, at inference, atarget emotion embedding is employed to convert the emotion of the inpututterance to the given target emotion. As opposed to performing emotionconversion on categorical representations, we use a continuous arousaldimension to represent emotions while also achieving intensity control. Wevalidate the proposed methodology on a large in-the-wild dataset, theMSP-Podcast v1.10. Our results show that the proposed diffusion model is indeedcapable of synthesizing speech with a controllable target emotion. Crucially,the proposed approach shows improved performance along the extreme values ofarousal and thereby addresses a common challenge in the speech emotionconversion literature.

EMOCONV-DIFF: Diffusion-based Speech Emotion Conversion for Non-parallel and In-the-wild Data