activity
20172026
most citedListen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End

11 citations · 17 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems

Yuepeng Hu, Pegah Ghahremani, Reza Aghajani +2

Multi-agent systems are increasingly used to solve coding tasks by coordinating agents that share code and plans. These agents are often optimized with reinforcement learning (RL)…

cs.CY2026

Muse Spark Safety & Preparedness Report

Cristina Menghini, Peter Ney, Hamza Kwisaba +117

Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…

eess.AS2022★ 2 cited

Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation

Viet Anh Trinh, Pegah Ghahremani, Brian King +3

We present an approach to reduce the performance disparity between geographic regions without degrading performance on the overall user population for ASR. A popular approach is to…

eess.AS2021★ 11 cited

Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End

Swayambhu Nath Ray, Minhua Wu, Anirudh Raju +7

Comprehending the overall intent of an utterance helps a listener recognize the individual words spoken. Inspired by this fact, we perform a novel study of the impact of explicitly…

cs.CL2017★ 4 cited

An Empirical Evaluation of Zero Resource Acoustic Unit Discovery

Chunxi Liu, Jinyi Yang, Ming Sun +7

Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. A…