Speech Intelligibility Classifiers from 550k Disordered Speech Samples

no code implementations • 13 Mar 2023 • Subhashini Venugopalan, Jimmy Tobin, Samuel J. Yang, Katie Seaver, Richard J. N. Cave, Pan-Pan Jiang, Neil Zeghidour, Rus Heywood, Jordan Green, Michael P. Brenner

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 disorders and rated for their overall intelligibility on a five-point scale.

Physics-enhanced machine learning for virtual fluorescence microscopy

1 code implementation • ICCV 2021 • Colin L. Cooke, Fanjie Kong, Amey Chaware, Kevin C. Zhou, Kanghyun Kim, Rong Xu, D. Michael Ando, Samuel J. Yang, Pavan Chandra Konda, Roarke Horstmeyer

This paper introduces a new method of data-driven microscope design for virtual fluorescence microscopy.

BIG-bench Machine Learning

Correcting Nuisance Variation using Wasserstein Distance

no code implementations • ICLR 2018 • Gil Tabak, Minjie Fan, Samuel J. Yang, Stephan Hoyer, Geoff Davis

One motivating application is drug development: morphological cell features can be captured from images, from which similarities between different drug compounds applied at different doses can be quantified.