117 citations · 150 across the 8 of their papers we have counts for
8 papers
The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models
Ariel Goldstein, Eric Ham, Mariano Schain +17
Deep Language Models (DLMs) provide a novel computational paradigm for understanding the mechanisms of natural language processing in the human brain. Unlike traditional psycholing…
Speech Intelligibility Classifiers from 550k Disordered Speech Samples
Subhashini Venugopalan, Jimmy Tobin, Samuel J. Yang +7
We developed dysarthric speech intelligibility classifiers on 551,176 disordered speech samples contributed by a diverse set of 468 speakers, with a range of self-reported speaking…
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…
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…
It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets
Subhashini Venugopalan, Arunachalam Narayanaswamy, Samuel Yang +10
Confounding variables are a well known source of nuisance in biomedical studies. They present an even greater challenge when we combine them with black-box machine learning techniq…
Personalizing ASR for Dysarthric and Accented Speech with Limited Data
Joel Shor, Dotan Emanuel, Oran Lang +9
Automatic speech recognition (ASR) systems have dramatically improved over the last few years. ASR systems are most often trained from 'typical' speech, which means that underrepre…