DyShip：利用声学特征从双人对话中分类说话人关系
Dyship: Leveraging Acoustic Features For Speaker Relationship Classification From Dyadic Conversations
AI 导读
研究者提出 DyShip 框架，基于 Seamless Interaction 语料库从双人对话中分类说话人的熟悉度与亲缘关系，对比了频谱图、MFCC、WavLM、x-vectors 和发音动作五种语音表示。
正文 · 原文
Abstract:We introduce DyShip, a framework for classifying speaker familiarity and kinship from dyadic conversations in the Seamless Interaction corpus. We compare five speech representations: spectrograms, MFCCs, self-supervised WavLM representations, x-vectors, and estimated articulatory movements. Spectral features perform best for familiarity classification, reaching an accuracy of 87.81%, while WavLM achieves the highest kinship accuracy of 85.17%. X-vectors and articulatory features yield weaker results on familiarity classifiaction, but shine for kinship classification, with an AUC of 0.909 for x-vectors. Most classifiers exceed previously reported human and audio language model scores on the same dataset. These findings highlight the potential of conventional acoustic features for relationship inference and motivate further investigation of the conversational cues captured by different representations.
| Comments: | 5 pages, under review |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14241 [cs.CL] |
| (or arXiv:2611.14241v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14241 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Thomas Thebaud [view email]
[v1]
Tue, 10 Nov 2026 15:53:43 UTC (351 KB)
来源：arXiv cs.AI · arxiv.org