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 dataaugmentation 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

Looking within the self: Investigating the Impact of Data Augmentation with Self-training on Automatic Speech Recognition for Hupa

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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Nitin Venkateswaran

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Looking within the self: Investigating the Impact of Data Augmentation with Self-training on Automatic Speech Recognition for Hupa

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