activity
20212024
most citedPerceptual Loss with Recognition Model for Single-Channel Enhancement and Robust ASR

8 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2024

Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers

Prashant Serai, Peidong Wang, Eric Fosler-Lussier

Modeling the errors of a speech recognizer can help simulate errorful recognized speech data from plain text, which has proven useful for tasks like discriminative language modelin…

cs.CL2024

A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models

Jaylen Jones, Lingbo Mo, Eric Fosler-Lussier +1

Counter narratives - informed responses to hate speech contexts designed to refute hateful claims and de-escalate encounters - have emerged as an effective hate speech intervention…

eess.AS2023

End-to-End real time tracking of children's reading with pointer network

Vishal Sunder, Beulah Karrolla, Eric Fosler-Lussier

In this work, we explore how a real time reading tracker can be built efficiently for children's voices. While previously proposed reading trackers focused on ASR-based cascaded ap…

cs.CL2023

Selective Demonstrations for Cross-domain Text-to-SQL

Shuaichen Chang, Eric Fosler-Lussier

Large language models (LLMs) with in-context learning have demonstrated impressive generalization capabilities in the cross-domain text-to-SQL task, without the use of in-domain an…

cs.SD20218 cited

Perceptual Loss with Recognition Model for Single-Channel Enhancement and Robust ASR

Peter Plantinga, Deblin Bagchi, Eric Fosler-Lussier

Single-channel speech enhancement approaches do not always improve automatic recognition rates in the presence of noise, because they can introduce distortions unhelpful for recogn…