• Corpus ID: 239049755

SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training

@article{Bapna2021SLAMAU,
  title={SLAM: A Unified Encoder for Speech and Language Modeling via Speech-Text Joint Pre-Training},
  author={Ankur Bapna and Yu-An Chung and Na Wu and Anmol Gulati and Ye Jia and J. Clark and Melvin Johnson and Jason Riesa and Alexis Conneau and Yu Zhang},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10329}
}
Unsupervised pre-training is now the predominant approach for both text and speech understanding. Self-attention models pre-trained on large amounts of unannotated data have been hugely successful when fine-tuned on downstream tasks from a variety of domains and languages. This paper takes the universality of unsupervised language pre-training one step further, by unifying speech and text pre-training within a single model. We build a single encoder with the BERT objective on unlabeled text… 

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