N-gram language models (LMs) are the innovation that first made large-vocabulary continuous automatic speech recognition (ASR) viable. With neural end-to-end ASR architectures, however, LMs have become an afterthought. While the effect on accuracy may be negligible for English and Mandarin, jettisoning the LM might not make sense for the world’s remaining 6000+ languages. In this paper, we investigate the role of the LM in low-resource ASR. First we ask: does using an n-gram LM in decoding in neural architectures help ASR performance? While it may seem obvious that it should, its absence in most implementations suggests otherwise. Second, we ask: when an n-gram LM is used in ASR, is there a relationship between the size of the LM and ASR accuracy? We have discovered that gut feelings on this question vary considerably, but there is little empirical work to support any particular claim. We explore these questions “in the wild” using a deliberately diverse set of 9 very small ASR corpora. The results show that: (1) decoding with an n-gram LM, regardless of its size, leads to lower word error rates; and (2) increasing the size of the LM appears to yield improvements only when the audio corpus itself is already relatively large. This suggests that collecting additional LM training text may benefit widely-spoken languages which typically have larger audio corpora. In contrast, for endangered languages where data of any kind will always be limited, efforts may be better spent collecting additional transcribed audio.

We investigate the performance of state-of-the-art neural ASR systems in transcribing audio recordings for Hupa, a critically endangered language of the Hoopa Valley Tribe. We also explore the impact on ASR performance when augmenting a small dataset of gold-standard high-quality transcriptions with a) a larger dataset with transcriptions of lower quality, and b) model-generated transcriptions in a self-training approach. An evaluation of both data augmentation approaches shows that the self-training approach is competitive, producing better WER scores than models trained with no additional data and not lagging far behind models trained with additional lower quality manual transcriptions instead: the deterioration in WER score is just 4.85 points when all the additional data is used in experiments with the best performing system, Wav2Vec. These findings have encouraging implications on the use of ASR systems for transcription and language documentation efforts in the Hupa language.

We present a completed, publicly available corpus of annotated semantic relations of adpositions and case markers in Hindi. We used the multilingual SNACS annotation scheme, which has been applied to a variety of typologically diverse languages. Building on past work examining linguistic problems in SNACS annotation, we use language models to attempt automatic labelling of SNACS supersenses in Hindi and achieve results competitive with past work on English. We look towards upstream applications in semantic role labelling and extension to related languages such as Gujarati.