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Expressive Visual Text-To-Speech as an Assistive Technology for Individuals with Autism Spectrum Conditions

机译:表现力的语音合成语音作为自闭症患者的辅助技术

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摘要

Adults with Autism Spectrum Conditions (ASC) experience marked difficulties in recognizing the emotions of others and responding appropriately. The clinical characteristics of ASC mean that face to face or group interventions may not be appropriate for this clinical group. This article explores the potential of a new interactive technology, converting text to emotionally expressive speech, to improve emotion processing ability and attention to faces in adults with ASC. We demonstrate a method for generating a near-videorealistic avatar (XpressiveTalk), which can produce a video of a face uttering inputted text, in a large variety of emotional tones. We then demonstrate that general population adults can correctly recognize the emotions portrayed by XpressiveTalk. Adults with ASC are significantly lessaccurate than controls, but still above chance levels for inferring emotions from XpressiveTalk. Both groups are significantly more accurate when inferring sad emotions from XpressiveTalk compared to the original actress, and rate these expressions as significantly more preferred and realistic. The potential applications for XpressiveTalk as an assistive technology for adults with ASC is discussed.
机译:患有自闭症谱系障碍(ASC)的成年人在识别他人的情绪和做出适当反应方面遇到明显的困难。 ASC的临床特征意味着面对面或小组干预可能不适用于该临床小组。本文探讨了一种新的交互式技术的潜力,该技术将文本转换为情感表达语音,以提高ASC成人的情感处理能力和对面部的关注。我们演示了一种生成接近视频现实的化身(XpressiveTalk)的方法,该化身可以产生表情各异的视频,并以多种情感语调表达。然后,我们证明普通人群成年人可以正确识别XpressiveTalk所描绘的情绪。成人ASC的准确率明显低于对照组,但仍高于从XpressiveTalk推断情绪的机会水平。与原始女演员相比,当从XpressiveTalk推断悲伤情绪时,两组人的准确性都明显更高,并且将这些表情评价为更可取,更现实。讨论了XpressiveTalk作为成人ASC辅助技术的潜在应用。

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