English(EN) Dyship: Leveraging Acoustic Features For Speaker Relationship Classification From Dyadic Conversations

新框架利用声学特征对说话人关系进行分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了DyShip，一个利用二元对话中的声学特征对说话人关系（如熟悉度和亲属关系）进行分类的新框架。该研究比较了五种不同的语音表示方法，发现光谱特征在熟悉度分类方面达到了87.81%的准确率，而WavLM在亲属关系分类方面达到了85.17%的准确率。DyShip框架的分类器在同一数据集上优于先前的人类和音频语言模型得分，表明声学线索在推断对话关系方面具有巨大潜力。 AI

影响这项研究展示了改进的对话动态分析方法，可能增强社交机器人、心理健康监测和人机交互等应用。

排序理由该集群包含一篇详细介绍新框架和实验结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Kieran Sturges, Thomas Thebaud, Yaohan Guan, Najim Dehak · 2026-11-11 05:00

Dyship：利用声学特征对双向对话中的说话人关系进行分类

arXiv:2611.14241v1 Announce Type: cross 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 repr…

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Dyship：利用声学特征对双向对话中的说话人关系进行分类

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