Although Germany has a diverse landscape of dialects, they are underrepresented in current automatic speech recognition (ASR) research. To enable studies of how robust models are towards dialectal var...

Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include i...

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Automatic speech recognition tools have potential for facilitating language documentation, but in practice these tools remain little-used by linguists for a variety of reasons, such as that the technology is still new (and evolving rapidly), user-friendly interfaces are still under development, and case studies demonstrating the practical usefulness of automatic recognition in a low-resource setting remain few. This article reports on a success story in integrating automatic transcription into the language documentation workflow, specifically for Yongning Na, a language of Southwest China. Using Persephone, an open-source toolkit, a single-speaker speech transcription tool was trained over five hours of manually transcribed speech. The experiments found that this method can achieve a remarkably low error rate (on the order of 17%), and that automatic transcriptions were useful as a canvas for the linguist. The present report is intended for linguists with little or no knowledge of speech processing. It aims to provide insights into (i) the way the tool operates and (ii) the process of collaborating with natural language processing specialists. Practical recommendations are offered on how to anticipate the requirements of this type of technology from the early stages of data collection in the field.