4 citations · 8 across the 6 of their papers we have counts for
9 papers
Assessing ASR Model Quality on Disordered Speech using BERTScore
Jimmy Tobin, Qisheng Li, Subhashini Venugopalan +3
Word Error Rate (WER) is the primary metric used to assess automatic speech recognition (ASR) model quality. It has been shown that ASR models tend to have much higher WER on speak…
Context-Aware Abbreviation Expansion Using Large Language Models
Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek +3
Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phras…
Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images
Katy Blumer, Subhashini Venugopalan, Michael P. Brenner +1
We analyze a dataset of retinal images using linear probes: linear regression models trained on some "target" task, using embeddings from a deep convolutional (CNN) model trained o…
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Subhashini Venugopalan, Joel Shor, Manoj Plakal +4
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment. Classification approaches can also help…
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
Andrei Kapishnikov, Subhashini Venugopalan, Besim Avci +3
Integrated Gradients (IG) is a commonly used feature attribution method for deep neural networks. While IG has many desirable properties, the method often produces spurious/noisy p…
Scientific Discovery by Generating Counterfactuals using Image Translation
Arunachalam Narayanaswamy, Subhashini Venugopalan, Dale R. Webster +10
Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent. Here we investigate if explanation tech…