发表机构
Fraunhofer Institute for Secure Information Technology SIT（弗劳恩霍夫安全信息技术研究所SIT）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对德语视频转录文本的作者身份验证缺口，实验发现传统n元语法AV方法在该任务上优于Transformer方法，验证了传统AV方法的竞争力。
AI 中文摘要
作者身份验证（Authorship Verification，AV）是数字文本取证的重要子领域，核心问题是判断两段文本是否出自同一作者之手。尽管该领域在过去二十年取得了显著进展，但仍存在若干未解决或未充分探索的重要挑战。例如，大多数AV研究聚焦于书面文本，然而语言不仅以书面形式存在，也以口语形式呈现，比如视频中的内容。此外，现有AV研究主要集中于英语，而德语等其他语言受到的关注相对较少。为解决这些研究缺口，我们将AV应用于德语视频转录形式的口语文本，检验现有成熟AV方法在跨视频对验证说话人身份的有效性。我们的实验评估基于10种AV方法，应用于3个自行编译的语料库（包含150位说话人的300个视频），结果显示，基于简单字符和词n元语法表示的传统AV方法取得了最佳性能，准确率最高达88%，AUC最高达90%。相比之下，更现代的基于Transformer的方法在所有评估语料库上的表现都显著更差。因此，我们的结果表明，AV领域的传统方法仍具有竞争力和相关性。
英文摘要
Authorship Verification (AV) represents an important subfield of digital text forensics and addresses the fundamental question of whether two texts were written by the same author. Although the field has made substantial progress over the past two decades, several important challenges remain unresolved or underexplored. For instance, most AV research has focused on written texts, despite the fact that language is expressed not only in written but also in spoken form, such as in videos. Moreover, existing AV studies have predominantly concentrated on English, while other languages, including German, have received comparatively little attention. To address these research gaps, we apply AV to spoken language in the form of transcripts of German-language videos and examine the effectiveness of established AV methods in verifying a speaker's identity across video pairs. Our experimental evaluation, based on a total of ten AV methods applied to three self-compiled corpora comprising 300 videos from 150 speakers, shows that the best performance (up to 88% accuracy and 90% AUC) is achieved by traditional AV approaches based on simple character- and token n-gram representations. In contrast, more modern transformer-based approaches perform significantly worse on all evaluated corpora. Our results therefore suggest that traditional methods in the field of AV remain both competitive and relevant.
Comments6 pages, planning to submit to WIFS 2026
