  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).
