14 citations · 15 across the 6 of their papers we have counts for
6 papers
Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking
Christopher Richardson, Roshan Sharma, Neeraj Gaur +3
Zero-shot domain adaptation for dialogue state tracking (DST) remains a challenging problem in task-oriented dialogue (TOD) systems, where models must generalize to target domains…
Speech Prefix-Tuning with RNNT Loss for Improving LLM Predictions
Murali Karthick Baskar, Andrew Rosenberg, Bhuvana Ramabhadran +2
In this paper, we focus on addressing the constraints faced when applying LLMs to ASR. Recent works utilize prefixLM-type models, which directly apply speech as a prefix to LLMs fo…
ASTRA: Aligning Speech and Text Representations for Asr without Sampling
Neeraj Gaur, Rohan Agrawal, Gary Wang +3
This paper introduces ASTRA, a novel method for improving Automatic Speech Recognition (ASR) through text injection.Unlike prevailing techniques, ASTRA eliminates the need for samp…
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition
Hillary Ngai, Rohan Agrawal, Neeraj Gaur +3
Adapters are an efficient, composable alternative to full fine-tuning of pre-trained models and help scale the deployment of large ASR models to many tasks. In practice, a task ID…
Improving Rare Word Recognition with LM-aware MWER Training
Weiran Wang, Tongzhou Chen, Tara N. Sainath +10
Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the…
From Audio to Semantics: Approaches to end-to-end spoken language understanding
Parisa Haghani, Arun Narayanan, Michiel Bacchiani +6
Conventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to a transcript, and a natural languag…