More than 7,000 known languages are spoken around the world. However, due to
the lack of annotated resources, only a small fraction of them are currently
covered by speech technologies. Albeit self-supervised speech representations,
recent massive speech corpora collections, as well as the organization of
challenges, have alleviated this inequality, most studies are mainly
benchmarked on English. This situation is aggravated when tasks involving both
acoustic and visual speech modalities are addressed. In order to promote
research on low-resource languages for audio-visual speech technologies, we
present AnnoTheia, a semi-automatic annotation toolkit that detects when a
person speaks on the scene and the corresponding transcription. In addition, to
show the complete process of preparing AnnoTheia for a language of interest, we
also describe the adaptation of a pre-trained model for active speaker
detection to Spanish, using a database not initially conceived for this type of
task. The AnnoTheia toolkit, tutorials, and pre-trained models are available on
GitHub.

为了推动低资源语言的视听说技术的研究，我们提出了 AnnoTheia，这是一个半自动注释工具包，可以检测场景中人们的说话以及相应的转录。此外，我们还介绍了将一个预训练模型用于西班牙语的活动说话者检测的适应过程，使用了最初并非为此类任务而设计的数据库。AnnoTheia 工具包、教程和预训练模型已经在 GitHub 上提供。