脚本化视听内容的说话人日志分离
Speaker Diarization of Scripted Audiovisual Content
AI总结：
针对电视节目说话人日志分离在跟踪多说话人和检测频繁切换上准确率低的问题，提出利用制作剧本提取伪标注数据的半监督方法，在66个节目测试集上相对无监督基线提升51.7%。
摘要：
媒体本地化行业通常需要最终电影或电视节目的逐字剧本，以便制作外语字幕或配音剧本。具体而言，逐字剧本（即播出剧本）必须被组织成一系列对话台词，每条包含时间码、说话人姓名和文本。当前的语音识别技术减轻了转录步骤。然而，最先进的说话人日志分离模型在电视节目上仍有不足，主要原因有两点：(i) 无法跟踪大量说话人，(ii) 在检测频繁的说话人变化方面准确率低。为缓解此问题，我们提出了一种新方法，利用拍摄过程中使用的制作剧本，为说话人日志分离任务提取伪标注数据。我们提出了一种新颖的半监督方法，并在包含66个节目的测试集上，相较于两个无监督基线模型，在我们的指标上实现了51.7%的相对提升。
英文摘要：
The media localization industry usually requires a verbatim script of the final film or TV production in order to create subtitles or dubbing scripts in a foreign language. In particular, the verbatim script (i.e. as-broadcast script) must be structured into a sequence of dialogue lines each including time codes, speaker name and transcript. Current speech recognition technology alleviates the transcription step. However, state-of-the-art speaker diarization models still fall short on TV shows for two main reasons: (i) their inability to track a large number of speakers, (ii) their low accuracy in detecting frequent speaker changes. To mitigate this problem, we present a novel approach to leverage production scripts used during the shooting process, to extract pseudo-labeled data for the speaker diarization task. We propose a novel semi-supervised approach and demonstrate improvements of 51.7% relative to two unsupervised baseline models on our metrics on a 66 show test set.
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。
