Incorporating automatic speech recognition (ASR) into field linguistics workflows for language documentation has become increasingly common. While ASR performance has seen improvements in low-resource settings, obstacles remain when training models on data collected by documentary linguists. One notable challenge lies in the way that this data is curated. ASR datasets built from spontaneous speech are typically recorded in consistent settings and transcribed by native speakers following a set of well designed guidelines. In contrast, field linguists collect data in whatever format it is delivered by their language consultants and transcribe it as best they can given their language skills and the quality of the recording. This approach to data curation, while valuable for linguistic research, does not always align with the standards required for training robust ASR models. In this paper, we explore methods for identifying speech transcriptions in fieldwork data that may be unsuitable for training ASR models. We focus on two complimentary automated measures of transcription quality that can be used to identify transcripts with characteristics that are common in field data but could be detrimental to ASR training. We show that one of the metrics is highly effective at retrieving these types of transcriptions. Additionally, we find that filtering datasets using this metric of transcription quality reduces WER both in controlled experiments using simulated fieldwork with artificially corrupted data and in real fieldwork corpora.

Machine translation (MT) can be a useful technology for language documentation and for promoting language use in endangered language communities. Few endangered languages, however, have an existing parallel corpus large enough to train a reasonable MT model. In this paper, we re-purpose a wide range of diverse data sources containing Amis, English, and Mandarin text to serve as parallel corpora for training MT systems for Amis, one of the Indigenous languages of Taiwan. To supplement the small amount of Amis-English data, we produce synthetic Amis-English data by using a high quality MT system to generate English translations for the Mandarin side of the Amis-Mandarin corpus. Using two popular neural MT systems, OpenNMT and NLLB, we train models to translate between English and Amis, and Mandarin and Amis. We find that including synthetic data is helpful only when translating to English. In addition, we observe that neither MT architecture is consistently superior to other and that performance seems to vary according to the direction of translation and the amount of data used. These results indicate that MT is possible for an under-resourced language even without a formally prepared parallel corpus, but multiple training methods should be explored to produce optimal results.

While automatic speech recognition (ASR) now achieves human-level accuracy for a dozen or so languages, the majority of the world’s languages lack the resources needed to train robust ASR models. For many of these languages, the largest available source of transcribed speech data consists of recordings of the Bible. Bible recordings are appealingly large and well-structured resources, but they have notable limitations: the vocabulary and style are constrained, and the recordings are typically produced by a single speaker in a studio. These factors raise an important question: to what extent are Bible recordings useful for developing ASR models to transcribe contemporary naturalistic speech, the goal of most ASR applications? In this paper, we use Bible recordings alongside contemporary speech recordings to train ASR models in a selection of under-resourced and endangered languages. We find that models trained solely on Bible data yield shockingly weak performance when tested on contemporary everyday speech, even when compared to models trained on other (non-Bible) out-of-domain data. We identify one way of effectively leveraging Bible data in the ASR training pipeline via a two-stage training regime. Our results highlight the need to re-assess reported results relying exclusively on Bible data and to use Bible data carefully and judiciously.