Several domains own corresponding widely used feature extractors, such as ResNet, BERT, and GPT-x. These models are  pre-trained on large amounts of unlabelled data by self-supervision and can be effectively applied for downstream tasks. In the speech domain, wav2vec2.0 starts to show its powerful representation ability and feasibility of ultra-low resource speech recognition on Librispeech corpus . However, this model has not been tested on real spoken scenarios and languages other than English. To verify its universality over languages, we apply the released pre-trained models to solve low-resource speech recognition tasks in various spoken languages. We achieve more than 20 %DIFDELCMD < \\%%% %DIF < relative improvements in six languages compared with previous works. Among these languages, English improves up to 52.4\\%. Moreover, using coarse-grained modeling units, such as subword and character, achieves better results than the letter.abstract 
There are several domains that own corresponding widely used feature extractors, such as ResNet, BERT, and GPT-x. These models are usually pre-trained on large amounts of unlabeled data by self-supervision and can be effectively applied to downstream tasks. In the speech domain, wav2vec2.0 starts to show its powerful representation ability and feasibility of ultra-low resource speech recognition on the Librispeech corpus, which belongs to the audiobook domain . However, wav2vec2.0 has not been examined on real spoken scenarios and languages other than English. To verify its universality over languages, we apply pre-trained models to solve low-resource speech recognition tasks in various spoken languages. We achieve more than 20 %DIFDELCMD < \\%%% %DIF < relative improvements in six languages compared with previous works. Among these languages, English improves up to 52.4\\%. Moreover, using coarse-grained modeling units, such as subword and character, achieves better results than the letter.abstract \% relative improvements in six languages compared with previous work. Among these languages, English achieves a gain of 52.4\%. Moreover, using coarse-grained modeling units, such as subword or character, achieves better results than fine-grained modeling units, such as phone or letter.
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Several domains
There are several domains that
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usually
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unlabelled
unlabeled
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for
to
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Librispeech corpus
the Librispeech corpus, which belongs to the audiobook domain
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this model
wav2vec2.0
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tested
examined
D
the released
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\% relative improvements in six languages compared with previous work. Among these languages, English achieves a gain of 52.4\%. Moreover, using coarse-grained modeling units, such as subword or character, achieves better results than fine-grained modeling units, such as phone or letter.