Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages
This work addresses the challenge of low-resource speech recognition for researchers and developers working with diverse spoken languages, demonstrating significant improvements for specific language applications.
This paper applies the wav2vec2.0 model to low-resource speech recognition across six different spoken languages, achieving over 20% relative improvement compared to previous work. Notably, the English language saw a 52.4% gain, and coarse-grained modeling units performed better than fine-grained units.
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% 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.