The International Distributed Little Red Hen Lab, usually called “Red Hen Lab” or just “Red Hen”, is dedicated to research into multimodal communication. In this article, we introduce the Red Hen Audio Tagger (RHAT), a novel, publicly available open source platform developed by Red Hen Lab. RHAT employs deep learning models to tag audio elements frame by frame, generating metadata tags that can be utilized in various data formats for analysis. RHAT seamlessly integrates with widely used linguistic research tools like ELAN: the researcher can use RHAT to tag audio content automatically and display those tags alongside other ELAN annotation tiers. RHAT additionally complements existing Red Hen pipelines devoted to natural language processing, speech-to-text processing, body pose analysis, optical character recognition, named entity recognition, computer vision, semantic frame recognition, and so on. These cooperating Red Hen pipelines are research tools to advance the science of multimodal communication.

中文翻译：

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红母鸡音频标记器

国际分布式小红母鸡实验室，通常被称为“红母鸡实验室”或简称“红母鸡”，致力于多模态通信的研究。在本文中，我们介绍了 Red Hen Audio Tagger (RHAT)，这是一个由 Red Hen Lab 开发的新型公开开源平台。 RHAT 采用深度学习模型逐帧标记音频元素，生成可用于各种数据格式进行分析的元数据标签。 RHAT 与 ELAN 等广泛使用的语言研究工具无缝集成：研究人员可以使用 RHAT 自动标记音频内容，并将这些标签与其他 ELAN 注释层一起显示。 RHAT 还补充了现有的 Red Hen 管道，致力于自然语言处理、语音到文本处理、身体姿势分析、光学字符识别、命名实体识别、计算机视觉、语义框架识别等。这些合作的 Red Hen 管道是推进多模式通信科学的研究工具。