2 papers
eess.AS2020
Towards Learning a Universal Non-Semantic Representation of Speech
Joel Shor, Aren Jansen, Ronnie Maor +7
The ultimate goal of transfer learning is to reduce labeled data requirements by exploiting a pre-existing embedding model trained for different datasets or tasks. The visual and l…
cs.CL2019
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…