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

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...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Enrico Varano [verfasserIn] Konstantinos Vougioukas [verfasserIn] Pingchuan Ma [verfasserIn] Stavros Petridis [verfasserIn] Maja Pantic [verfasserIn] Tobias Reichenbach [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	speech perception audiovisual integration speech in noise facial animation generative adversarial network (GAN)

Übergeordnetes Werk:	In: Frontiers in Neuroscience - Frontiers Media S.A., 2008, 15(2022)
Übergeordnetes Werk:	volume:15 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnins.2021.781196

Katalog-ID:	DOAJ074595660

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language	English
source	In Frontiers in Neuroscience 15(2022) volume:15 year:2022
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topic_facet	speech perception audiovisual integration speech in noise facial animation generative adversarial network (GAN) Neurosciences. Biological psychiatry. Neuropsychiatry
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container_title	Frontiers in Neuroscience
authorswithroles_txt_mv	Enrico Varano @@aut@@ Konstantinos Vougioukas @@aut@@ Pingchuan Ma @@aut@@ Stavros Petridis @@aut@@ Maja Pantic @@aut@@ Tobias Reichenbach @@aut@@
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callnumber-first	R - Medicine
author	Enrico Varano
spellingShingle	Enrico Varano misc RC321-571 misc speech perception misc audiovisual integration misc speech in noise misc facial animation misc generative adversarial network (GAN) misc Neurosciences. Biological psychiatry. Neuropsychiatry Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans
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topic_title	RC321-571 Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans speech perception audiovisual integration speech in noise facial animation generative adversarial network (GAN)
topic	misc RC321-571 misc speech perception misc audiovisual integration misc speech in noise misc facial animation misc generative adversarial network (GAN) misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc speech perception misc audiovisual integration misc speech in noise misc facial animation misc generative adversarial network (GAN) misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans
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title_full	Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans
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author_browse	Enrico Varano Konstantinos Vougioukas Pingchuan Ma Stavros Petridis Maja Pantic Tobias Reichenbach
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title_sort	speech-driven facial animations improve speech-in-noise comprehension of humans
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title_auth	Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans
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.
abstractGer	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.
abstract_unstemmed	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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title_short	Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans
url	https://doi.org/10.3389/fnins.2021.781196 https://doaj.org/article/c7b91d57300a40df9ced1dee403dea73 https://www.frontiersin.org/articles/10.3389/fnins.2021.781196/full https://doaj.org/toc/1662-453X
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author2	Konstantinos Vougioukas Pingchuan Ma Stavros Petridis Maja Pantic Tobias Reichenbach
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