@inproceedings{cai-etal-2023-masked,
    title = "Masked Audio Text Encoders are Effective Multi-Modal Rescorers",
    author = "Cai, Jinglun  and
      Sunkara, Monica  and
      Li, Xilai  and
      Bhatia, Anshu  and
      Pan, Xiao  and
      Bodapati, Sravan",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.682",
    doi = "10.18653/v1/2023.findings-acl.682",
    pages = "10718--10730",
    abstract = "Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems. In this work, we propose Masked Audio Text Encoder (MATE), a multi-modal masked language model rescorer which incorporates acoustic representations into the input space of MLM. We adopt contrastive learning for effectively aligning the modalities by learning shared representations. We show that using a multi-modal rescorer is beneficial for domain generalization of the ASR system when target domain data is unavailable. MATE reduces word error rate (WER) by 4{\%}-16{\%} on in-domain, and 3{\%}-7{\%} on out-of-domain datasets, over the text-only baseline. Additionally, with very limited amount of training data (0.8 hours) MATE achieves a WER reduction of 8{\%}-23{\%} over the first-pass baseline.",
}

Masked Language Models (MLMs) have proven to be effective for second-pass rescoring in Automatic Speech Recognition (ASR) systems. In this work, we propose Masked Audio Text Encoder (MATE), a multi-modal masked language model rescorer which incorporates acoustic representations into the input space of MLM. We adopt contrastive learning for effectively aligning the modalities by learning shared representations. We show that using a multi-modal rescorer is beneficial for domain generalization of the ASR system when target domain data is unavailable. MATE reduces word error rate (WER) by 4{\%}-16{\%} on in-domain, and 3{\%}-7{\%} on out-of-domain datasets, over the text-only baseline. Additionally, with very limited amount of training data (0.8 hours) MATE achieves a WER reduction of 8{\%}-23{\%} over the first-pass baseline.
Cai, Jinglun Sunkara, Monica Li, Xilai Bhatia, Anshu Pan, Xiao Bodapati, Sravan
Masked Audio Text Encoders are Effective Multi-Modal Rescorers