Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans

Frontiers in Neuroscience

Tobias Reichenbach¹  Enrico Varano²  Konstantinos Vougioukas³  Stavros Petridis³  Pingchuan Ma³  Maja Pantic³ 

[1] Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany;Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom;Department of Computing, Imperial College London, London, United Kingdom;

关键词: speech perception;    audiovisual integration;    speech in noise;    facial animation;    generative adversarial network (GAN);   

DOI  :  10.3389/fnins.2021.781196

来源: DOAJ

【 摘 要 】

Understanding speech becomes a demanding task when the environment is noisy. Comprehension of speech in noise can be substantially improved by looking at the speaker’s face, and this audiovisual benefit is even more pronounced in people with hearing impairment. Recent advances in AI have allowed to synthesize photorealistic talking faces from a speech recording and a still image of a person’s face in an end-to-end manner. However, it has remained unknown whether such facial animations improve speech-in-noise comprehension. Here we consider facial animations produced by a recently introduced generative adversarial network (GAN), and show that humans cannot distinguish between the synthesized and the natural videos. Importantly, we then show that the end-to-end synthesized videos significantly aid humans in understanding speech in noise, although the natural facial motions yield a yet higher audiovisual benefit. We further find that an audiovisual speech recognizer (AVSR) benefits from the synthesized facial animations as well. Our results suggest that synthesizing facial motions from speech can be used to aid speech comprehension in difficult listening environments.

【 授权许可】

Unknown