38 citations · 124 across the 30 of their papers we have counts for
6 papers · 1 filter
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems
Saket Dingliwal, Ashish Shenoy, Sravan Bodapati +3
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 memor…
Lattention: Lattice-attention in ASR rescoring
Prabhat Pandey, Sergio Duarte Torres, Ali Orkan Bayer +2
Lattices form a compact representation of multiple hypotheses generated from an automatic speech recognition system and have been shown to improve performance of downstream tasks l…
Prompt-tuning in ASR systems for efficient domain-adaptation
Saket Dingliwal, Ashish Shenoy, Sravan Bodapati +3
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains. Since domain-specific systems perform better than their…
Attention-based Contextual Language Model Adaptation for Speech Recognition
Richard Diehl Martinez, Scott Novotney, Ivan Bulyko +3
Language modeling (LM) for automatic speech recognition (ASR) does not usually incorporate utterance level contextual information. For some domains like voice assistants, however,…
Domain-aware Neural Language Models for Speech Recognition
Linda Liu, Yile Gu, Aditya Gourav +5
As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains. We present a domain-…
Personalization Strategies for End-to-End Speech Recognition Systems
Aditya Gourav, Linda Liu, Ankur Gandhe +9
The recognition of personalized content, such as contact names, remains a challenging problem for end-to-end speech recognition systems. In this work, we demonstrate how first and…