53 citations · 92 across the 7 of their papers we have counts for
7 papers
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems
Hitesh Tulsiani, David M. Chan, Shalini Ghosh +6
Dialog systems, such as voice assistants, are expected to engage with users in complex, evolving conversations. Unfortunately, traditional automatic speech recognition (ASR) system…
Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue
Guan-Ting Lin, Prashanth Gurunath Shivakumar, Ankur Gandhe +6
Large Language Models (LLMs) have demonstrated superior abilities in tasks such as chatting, reasoning, and question-answering. However, standard LLMs may ignore crucial paralingui…
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
Kevin Everson, Yile Gu, Huck Yang +10
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with tr…
Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech Recognition
David M. Chan, Shalini Ghosh, Hitesh Tulsiani +2
While word error rates of automatic speech recognition (ASR) systems have consistently fallen, natural language understanding (NLU) applications built on top of ASR systems still a…
Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification
Anirudh S. Sundar, Chao-Han Huck Yang, David M. Chan +3
Training large foundation models using self-supervised objectives on unlabeled data, followed by fine-tuning on downstream tasks, has emerged as a standard procedure. Unfortunately…
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting
Chao-Han Huck Yang, Yile Gu, Yi-Chieh Liu +3
We explore the ability of large language models (LLMs) to act as speech recognition post-processors that perform rescoring and error correction. Our first focus is on instruction p…
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition
Yu Yu, Chao-Han Huck Yang, Jari Kolehmainen +15
We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring. Although pretrained language models (LMs) like BERT have s…