Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages

Understand

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.