There has been significant progress in emotional Text-To-Speech (TTS)
synthesis technology in recent years. However, existing methods primarily focus
on the synthesis of a limited number of emotion types and have achieved
unsatisfactory performance in intensity control. To address these limitations,
we propose EmoMix, which can generate emotional speech with specified intensity
or a mixture of emotions. Specifically, EmoMix is a controllable emotional TTS
model based on a diffusion probabilistic model and a pre-trained speech emotion
recognition (SER) model used to extract emotion embedding. Mixed emotion
synthesis is achieved by combining the noises predicted by diffusion model
conditioned on different emotions during only one sampling process at the
run-time. We further apply the Neutral and specific primary emotion mixed in
varying degrees to control intensity. Experimental results validate the
effectiveness of EmoMix for synthesizing mixed emotion and intensity control.Comment: Accepted by 24th Annual Conference of the International Speech
Communication Association (INTERSPEECH 2023