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Eye Gaze and Perceptual Adaptation to Audiovisual Degraded Speech

凝视 心理学 感知 适应(眼睛) 眼动 感觉线索 语音识别 听力学 认知心理学 沟通 计算机科学 人工智能 医学 神经科学 精神分析
作者
Briony Banks,Emma Gowen,Kevin J. Munro,Patti Adank
出处
期刊:Journal of Speech Language and Hearing Research [American Speech–Language–Hearing Association]
卷期号:64 (9): 3432-3445 被引量:8
标识
DOI:10.1044/2021_jslhr-21-00106
摘要

Purpose Visual cues from a speaker's face may benefit perceptual adaptation to degraded speech, but current evidence is limited. We aimed to replicate results from previous studies to establish the extent to which visual speech cues can lead to greater adaptation over time, extending existing results to a real-time adaptation paradigm (i.e., without a separate training period). A second aim was to investigate whether eye gaze patterns toward the speaker's mouth were related to better perception, hypothesizing that listeners who looked more at the speaker's mouth would show greater adaptation. Method A group of listeners (n = 30) was presented with 90 noise-vocoded sentences in audiovisual format, whereas a control group (n = 29) was presented with the audio signal only. Recognition accuracy was measured throughout and eye tracking was used to measure fixations toward the speaker's eyes and mouth in the audiovisual group. Results Previous studies were partially replicated: The audiovisual group had better recognition throughout and adapted slightly more rapidly, but both groups showed an equal amount of improvement overall. Longer fixations on the speaker's mouth in the audiovisual group were related to better overall accuracy. An exploratory analysis further demonstrated that the duration of fixations to the speaker's mouth decreased over time. Conclusions The results suggest that visual cues may not benefit adaptation to degraded speech as much as previously thought. Longer fixations on a speaker's mouth may play a role in successfully decoding visual speech cues; however, this will need to be confirmed in future research to fully understand how patterns of eye gaze are related to audiovisual speech recognition. All materials, data, and code are available at https://osf.io/2wqkf/.
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