98 citations · 238 across the 32 of their papers we have counts for
41 papers
In-Context Adaptation of Encoder-Decoder Models in Speech Recognition
Yen Meng, Sharon Goldwater, Hao Tang
In-context learning offers an appealing approach to adapt automatic speech recognition (ASR) models to new speakers, accents, and domains by providing speech-text pairs as demonstr…
A framework for analyzing concept representations in neural models
Burin Naowarat, Hao Tang, Sharon Goldwater
Understanding how neural models represent human-interpretable concepts is challenging. Prior work has explored linear concept subspaces from diverse perspectives, such as probing a…
Analyzing the relationships between pretraining language, phonetic, tonal, and speaker information in self-supervised speech models
Michele Gubian, Ioana Krehan, Oli Liu +2
Analyses of self-supervised speech models have begun to reveal where and how they represent different types of information. However, almost all analyses have focused on English. He…
Effective Context in Neural Speech Models
Yen Meng, Sharon Goldwater, Hao Tang
Modern neural speech models benefit from having longer context, and many approaches have been proposed to increase the maximum context a model can use. However, few have attempted…
Revisiting Common Assumptions about Arabic Dialects in NLP
Amr Keleg, Sharon Goldwater, Walid Magdy
Arabic has diverse dialects, where one dialect can be substantially different from the others. In the NLP literature, some assumptions about these dialects are widely adopted (e.g.…
Orthogonality and isotropy of speaker and phonetic information in self-supervised speech representations
Mukhtar Mohamed, Oli Danyi Liu, Hao Tang +1
Self-supervised speech representations can hugely benefit downstream speech technologies, yet the properties that make them useful are still poorly understood. Two candidate proper…