ISCA Archive Interspeech 2022
ISCA Archive Interspeech 2022

Domain Prompts: Towards memory and compute efficient domain adaptation of ASR systems

Saket Dingliwal, Ashish Shenoy, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, Katrin Kirchhoff

Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead. In this work, we introduce domain-prompts, a methodology that involves training a small number of domain embedding parameters to prime a Transformer-based Language Model (LM) to a particular domain. Using this domain-adapted LM for rescoring ASR hypotheses can achieve 7-13% WER reduction for a new domain with just 1000 unlabeled textual domain-specific sentences. This improvement is comparable or even better than fully fine-tuned models even though just 0.02% of the parameters of the base LM are updated. Additionally, our method is deployment-friendly as the learnt domain embeddings are prefixed to the input to the model rather than changing the base model architecture. Therefore, our method is an ideal choice for on-the-fly adaptation of LMs used in ASR systems to progressively scale it to new domains.


doi: 10.21437/Interspeech.2022-824

Cite as: Dingliwal, S., Shenoy, A., Bodapati, S., Gandhe, A., Gadde, R.T., Kirchhoff, K. (2022) Domain Prompts: Towards memory and compute efficient domain adaptation of ASR systems. Proc. Interspeech 2022, 684-688, doi: 10.21437/Interspeech.2022-824

@inproceedings{dingliwal22_interspeech,
  author={Saket Dingliwal and Ashish Shenoy and Sravan Bodapati and Ankur Gandhe and Ravi Teja Gadde and Katrin Kirchhoff},
  title={{Domain Prompts: Towards memory and compute efficient domain adaptation of ASR systems}},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={684--688},
  doi={10.21437/Interspeech.2022-824},
  issn={2958-1796}
}