UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

• URL: http://arxiv.org/abs/2101.07597v1
• Date: Tue, 19 Jan 2021 12:53:43 GMT
• Title: UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
• Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng and Xuedong Huang
• Abstract summary: We propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus.
• Score: 54.733889961024445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.

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