Towards Streaming Speech-to-Avatar Synthesis

Abstract: Streaming speech-to-avatar synthesis creates real-time animations for a virtual character from audio data. Accurate avatar representations of speech are important for the visualization of sound in linguistics, phonetics, and phonology, visual feedback to assist second language acquisition, and virtual embodiment for paralyzed patients. Previous works have highlighted the capability of deep articulatory inversion to perform high-quality avatar animation using electromagnetic articulography (EMA) features. However, these models focus on offline avatar synthesis with recordings rather than real-time audio, which is necessary for live avatar visualization or embodiment. To address this issue, we propose a method using articulatory inversion for streaming high quality facial and inner-mouth avatar animation from real-time audio. Our approach achieves 130ms average streaming latency for every 0.1 seconds of audio with a 0.792 correlation with ground truth articulations. Finally, we show generated mouth and tongue animations to demonstrate the efficacy of our methodology.