作者归属模型能否区分语音转录文本中的说话人？
Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?
AI总结：
针对语音转录文本说话人归属缺乏基准、现有书面文本模型受话题干扰大的问题，本文构建了控制话题关联的对话语音说话人归属新基准，对比多类基线模型确立SOTA，并分析了转录风格和微调的影响。
摘要：
作者验证是判定两份不同写作样本是否出自同一作者的任务，通常针对书面文本的归属展开研究。本文探索语音转录文本的归属问题，该任务带来了全新挑战。核心挑战在于，标点、大小写等许多文体特征在此场景下不具备区分效力。另一方面，语音转录文本呈现出其他模式，比如填充词和反馈语（如“um”“uh-huh”），这些可能是不同说话人的特征标识。我们提出了一个聚焦人工转录对话语音文本的说话人归属新基准。为了限制说话人与话题的虚假关联，我们同时采用对话提示语和参与同一场对话的说话人来构建不同难度的验证测试用例。我们通过对比一系列神经与非神经基线模型，在该新基准上确立了当前最优性能，研究发现尽管书面文本归属模型在特定场景下表现出人意料地好，但随着对话话题的控制程度不断提升，其性能会显著下降。我们还分析了转录风格对性能的影响，以及在语音转录文本上进行微调对性能的提升作用。
英文摘要：
Authorship verification is the task of determining if two distinct writing samples share the same author and is typically concerned with the attribution of written text. In this paper, we explore the attribution of transcribed speech, which poses novel challenges. The main challenge is that many stylistic features, such as punctuation and capitalization, are not informative in this setting. On the other hand, transcribed speech exhibits other patterns, such as filler words and backchannels (e.g., 'um', 'uh-huh'), which may be characteristic of different speakers. We propose a new benchmark for speaker attribution focused on human-transcribed conversational speech transcripts. To limit spurious associations of speakers with topic, we employ both conversation prompts and speakers participating in the same conversation to construct verification trials of varying difficulties. We establish the state of the art on this new benchmark by comparing a suite of neural and non-neural baselines, finding that although written text attribution models achieve surprisingly good performance in certain settings, they perform markedly worse as conversational topic is increasingly controlled. We present analyses of the impact of transcription style on performance as well as the ability of fine-tuning on speech transcripts to improve performance.
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。
