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In this paper, we propose a unified pre-training approach called UniSpeech tolearn speech representations with both unlabeled and labeled data, in whichsupervised phonetic CTC learning and phonetically-aware contrastiveself-supervised learning are conducted in a multi-task learning manner. Theresultant representations can capture information more correlated with phoneticstructures and improve the generalization across languages and domains. Weevaluate the effectiveness of UniSpeech for cross-lingual representationlearning on public CommonVoice corpus. The results show that UniSpeechoutperforms self-supervised pretraining and supervised transfer learning forspeech recognition by a maximum of 13.4% and 17.8% relative phone error ratereductions respectively (averaged over all testing languages). Thetransferability of UniSpeech is also demonstrated on a domain-shift speechrecognition task, i.e., a relative word error rate reduction of 6% against theprevious approach.

UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data