ArticleFrontiers in neuroscience2021
Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- The Cortical Contribution to the Speech-Frequency-Following Response Is Not Modulated by Visual Information.The European journal of neuroscience · 2026Article
- Articulatory timing and form support distinct neural benefits during audiovisual speech.bioRxiv : the preprint server for biology · 2026Article
- Stereoelectroencephalography Reveals Neural Signatures of Multisensory Integration in the Human Superior Temporal Sulcus during Audiovisual Speech Perception.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Synthetic faces generated with the facial action coding system or deep neural networks improve speech-in-noise perception, but not as much as real faces.Frontiers in neuroscience · 2024Article
- The noisy encoding of disparity model predicts perception of the McGurk effect in native Japanese speakers.Frontiers in neuroscience · 2024Article
- Article
- Speech-In-Noise Comprehension is Improved When Viewing a Deep-Neural-Network-Generated Talking Face.Trends in hearingArticle
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.