arXiv cs.CL· Thanushi Withanage, Desi Jones, Noah Sasson, Elizabeth Redcay, Carol Espy-Wilson·· 12 小时前AI 评分18
多模态感知对话成功度预测：面向自闭症与非自闭症者自发双人互动
Multi modal perceived conversation success prediction for spontaneous autistic and non-autistic dyadic interactions
AI 导读
研究利用发音、面部、声学韵律和轮次转换等多模态特征，在三个包含自闭症与非自闭症个体的自发双人互动数据集上预测参与者感知的对话成功度。结果表明对话成功本质上是多维的，需要多面向分析框架；融合互补模态可捕捉单一模态无法提供的信息，从而提升预测效果。
正文 · 原文
Abstract:Conversation success is important for communication, social bonds, and well-being and is often reduced in autistic-neurotypical conversations. Previous studies have predicted perceived conversation success using acoustic and lexical features in pediatric autism-enriched samples, or using nonverbal behavior in non-autistic group interactions. Other work has characterized prosodic, facial, motor, and turn-taking synchrony in autistic interactions, but has primarily targeted diagnostic classification or group differences. The present study instead predicts participant perceived conversation success from complementary articulatory, facial, acoustic-prosodic, and turn-taking measures across three spontaneous dyadic-interaction datasets including autistic and non autistic individuals. By leveraging multimodal features, we demonstrate that conversational success is inherently multidimensional and requires a multi-faceted analytical framework. Combining these complementary modalities offers the potential to improve prediction of perceived conversation success by capturing information unavailable from any single modality.
| Comments: | Submitted to ICASSP 2027 |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.31682 [cs.CL] |
| (or arXiv:2610.31682v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31682 arXiv-issued DOI via DataCite |
Submission history
From: Thanushi Withanage [view email]
[v1]
Fri, 2 Oct 2026 21:47:30 UTC (129 KB)
来源：arXiv cs.CL · arxiv.org