UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

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ID: 282592
2021
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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.
Reference Key
huang2021unispeech Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chengyi Wang; Yu Wu; Yao Qian; Kenichi Kumatani; Shujie Liu; Furu Wei; Michael Zeng; Xuedong Huang
Journal arXiv
Year 2021
DOI
DOI not found
URL
http://arxiv.org/abs/2101.07597v2
Keywords
cs.lg cs.cl eess.as cs.sd

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