In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis

• URL: http://arxiv.org/abs/2306.01916v1
• Date: Fri, 2 Jun 2023 21:02:51 GMT
• Title: In-the-wild Speech Emotion Conversion Using Disentangled Self-Supervised Representations and Neural Vocoder-based Resynthesis
• Authors: Navin Raj Prabhu, Nale Lehmann-Willenbrock and Timo Gerkmann
• Abstract summary: We introduce a methodology that uses self-supervised networks to disentangle the lexical, speaker, and emotional content of the utterance. We then use a HiFiGAN vocoder to resynthesise the disentangled representations to a speech signal of the targeted emotion. Results reveal that the proposed approach is aptly conditioned on the emotional content of input speech and is capable of synthesising natural-sounding speech for a target emotion.
• Score: 15.16865739526702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech emotion conversion aims to convert the expressed emotion of a spoken utterance to a target emotion while preserving the lexical information and the speaker's identity. In this work, we specifically focus on in-the-wild emotion conversion where parallel data does not exist, and the problem of disentangling lexical, speaker, and emotion information arises. In this paper, we introduce a methodology that uses self-supervised networks to disentangle the lexical, speaker, and emotional content of the utterance, and subsequently uses a HiFiGAN vocoder to resynthesise the disentangled representations to a speech signal of the targeted emotion. For better representation and to achieve emotion intensity control, we specifically focus on the aro\-usal dimension of continuous representations, as opposed to performing emotion conversion on categorical representations. We test our methodology on the large in-the-wild MSP-Podcast dataset. Results reveal that the proposed approach is aptly conditioned on the emotional content of input speech and is capable of synthesising natural-sounding speech for a target emotion. Results further reveal that the methodology better synthesises speech for mid-scale arousal (2 to 6) than for extreme arousal (1 and 7).

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