Editing State-of-the-art (section)

==== Yi, C., Wang, J., Cheng, N., Zhou, S., & Xu, B. (2021). ''Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages'' (arXiv:2012.12121). arXiv. <nowiki>http://arxiv.org/abs/2012.12121</nowiki> ====
*Summary:  The authors applied the pre-trained wav2vec2.0 model to low-resource speech recognition across six languages. Despite being pre-trained on a different domain, wav2vec2.0 could effectively adapt when fine-tuned on limited transcribed speech, even outperforming supervised pre-training approaches. Using coarser modeling units like subwords/characters worked better than finer units like phonemes/letters. Critically, self-supervised pre-training on large unlabeled data enabled wav2vec2.0 to learn robust speech representations that transferred well across languages and domains, showcasing its impressive potential for tackling low-resource speech tasks.
*RQ: Can the pre-trained wav2vec2.0 model, which was trained on English audiobook data, be effectively applied to low-resource speech recognition tasks in various languages and real-world spoken scenarios?
* Hypothesis: The self-supervised pre-training of wav2vec2.0 allows it to learn general acoustic representations that can be adapted to different languages and domains, even with limited transcribed data.
* Conclusion:  The experiments demonstrate that wav2vec2.0 can achieve significant performance improvements on low-resource speech recognition tasks across six languages (Arabic, English, Mandarin, Japanese, German, and Spanish) compared to previous methods. The largest gain of 52.4% was observed for English, likely due to the pre-training data being in English. Using coarser-grained modeling units like subwords or characters generally performed better than finer-grained units like phones or letters.
* Critical observations:
*# Self-supervised pre-training on a large amount of unlabeled data from other languages can be more effective than supervised pre-training on limited target language data.
*# The encoder-decoder structure did not perform well in low-resource scenarios, possibly due to the decoder's inability to generalize from sparse transcriptions.
*# External language models provided significant performance gains across all languages, model sizes, and modeling units.

*Relevance:  This research highlights the potential of self-supervised pre-trained models like wav2vec2.0 to alleviate the data scarcity problem in low-resource speech recognition tasks. It demonstrates the model's ability to adapt to various languages and spoken domains, even when pre-trained on data from a different domain (audiobooks). The findings suggest that large-scale self-supervised pre-training can learn robust acoustic representations that can be effectively transferred to downstream tasks with limited data.