检测法语电视与广播内容中口语互动的话轮边界终结性
Detecting the terminality of speech-turn boundary for spoken interactions in French TV and Radio content
AI总结：
本文针对法语电视与广播多说话者语料，基于预训练自监督表征比较音频、文本及融合模型，以检测话轮边界是否终结，并分析融合策略、上下文长度和随机初始化对性能的影响。
摘要：
Transition Relevance Places（话轮转换相关位置）被定义为话语的终点，在该位置对话者可以接过话轮而不会打断当前说话者——即话轮处于终结状态的位置。分析话轮终结性有助于研究自然会话中话轮转换的动态过程。本文提出在多说话者场景下将口语话语自动分类为 Terminal（终结）或 Non-Terminal（非终结）。我们在一个法语电视和广播片段语料库上比较了音频、文本以及二者融合的方法，该语料库在每次说话者切换处都标注了话轮终结性信息。我们的模型基于预训练的自监督表征。论文报告了不同融合策略和不同上下文长度下的结果。本研究还通过分析多次随机初始化训练运行之间的结果差异，探讨了性能波动问题。所测得的准确率表明，这些模型可用于大规模话轮转换分析。
英文摘要：
Transition Relevance Places are defined as the end of an utterance where the interlocutor may take the floor without interrupting the current speaker --i.e., a place where the turn is terminal. Analyzing turn terminality is useful to study the dynamic of turn-taking in spontaneous conversations. This paper presents an automatic classification of spoken utterances as Terminal or Non-Terminal in multi-speaker settings. We compared audio, text, and fusions of both approaches on a French corpus of TV and Radio extracts annotated with turn-terminality information at each speaker change. Our models are based on pre-trained self-supervised representations. We report results for different fusion strategies and varying context sizes. This study also questions the problem of performance variability by analyzing the differences in results for multiple training runs with random initialization. The measured accuracy would allow the use of these models for large-scale analysis of turn-taking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
