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 · Interspeech 2022 · 2022
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 and a handful of additional parameters. Our method can match or even beat the performance of models fully fine-tuned towards a particular domain with only 0.02% of the parameters. Given the parameter efficiency and negligible deployment overhead, our experiments showcase that such a method is an ideal choice for on-the-fly adaptation of LMs used in ASR systems to progressively scale it to new domains.