1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Anticipation?
Yueyi Yang, Haotian Liu, Fang Kang +4
We explore the use of large language models (LLMs) for next-utterance anticipation in human dialogue. Despite recent advances in LLMs demonstrating their ability to engage in natur…
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…
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…
A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech
Oli Danyi Liu, Hao Tang, Naomi Feldman +1
Speech perception involves storing and integrating sequentially presented items. Recent work in cognitive neuroscience has identified temporal and contextual characteristics in hum…