7 citations · 10 across the 19 of their papers we have counts for
11 papers · 1 filter
Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback
Guan-Ting Lin, Prashanth Gurunath Shivakumar, Aditya Gourav +4
While textless Spoken Language Models (SLMs) have shown potential in end-to-end speech-to-speech modeling, they still lag behind text-based Large Language Models (LLMs) in terms of…
USTED: Improving ASR with a Unified Speech and Text Encoder-Decoder
Bolaji Yusuf, Ankur Gandhe, Alex Sokolov
Improving end-to-end speech recognition by incorporating external text data has been a longstanding research topic. There has been a recent focus on training E2E ASR models that ge…
A Likelihood Ratio based Domain Adaptation Method for E2E Models
Chhavi Choudhury, Ankur Gandhe, Xiaohan Ding +1
End-to-end (E2E) automatic speech recognition models like Recurrent Neural Networks Transducer (RNN-T) are becoming a popular choice for streaming ASR applications like voice assis…
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-…