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UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

Chengyi Wang · Yu Wu · Yao Qian · Kenichi Kumatani · Shujie Liu · Furu Wei · Michael Zeng · Xuedong Huang

Keywords: [ Speech Recognition ]


In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both labeled and unlabeled 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 26.9\% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also verified 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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