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The task of speaker diarization aims to determine which speakers spoke when in a recording. Such functionality could help to accelerate work in endangered languages by facilitating transcription and semi-automatically extracting useful meta-data to enrich language archives. However, there has been little work on speaker diarization for low-resource or endangered languages. This work explores three neural approaches to speaker diarization applied to data sets drawn from endangered language archives. We find consistent improvements for recent neural x-vector models over earlier approaches. We also assess the factors which impact performance across models and data sets, with a focus on the challenging characteristics of endangered language recordings. 

Advances in speech and language processing have enabled the creation of applications that could, in principle, accelerate the process of language documentation, as speech communities and linguists work on urgent language documentation and reclamationnprojects. However, such systems have yet to make a significant impact on language documentation, as resource requirements limit the broad applicability of these new techniques. We aim to exploit the framework of shared tasks to focus the technology research community on tasks which address key pain points in language documentation. Here we presentninitial steps in the implementation of these new shared tasks, through the creation of data sets drawn from endangered language repositories and baseline systems to perform segmentation and speaker labeling of these audio recordings—important enabling steps in the documentation process. This paper motivates these tasks with a use case, describes data set curation and baseline systems, and presents results on this data. We then highlight the challenges and ethical considerations in developing these speech processing tools and tasks to support endangered language documentation.