1 citations · 2 across the 11 of their papers we have counts for
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Learning New Words from Unlabeled Test Data in Automatic Speech Recognition
Mengqi Wang, Mark A. Hasegawa-Johnson, Haolong Zheng +1
New words are invented every day. A human listener can learn a new word by hearing it clearly once and inferring its usage from sentence context. This paper proposes granting ASR a…
SiamCTC: Learning Speech Representations through Monotonic Temporal Alignment
SooHwan Eom, Mark Hasegawa-Johnson, Chang D. Yoo
Self-supervised speech representation learning has made significant progress through Siamese networks, which leverage different views of the same input. However, existing methods o…
Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech
Jaesung Bae, Xiuwen Zheng, Minje Kim +2
Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evaluation is costly and difficult to scale,…
Towards Robust Dysarthric Speech Recognition: LLM-Agent Post-ASR Correction Beyond WER
Xiuwen Zheng, Sixun Dong, Bornali Phukon +2
While Automatic Speech Recognition (ASR) is typically benchmarked by word error rate (WER), real-world applications ultimately hinge on semantic fidelity. This mismatch is particul…
A Theory of Unsupervised Speech Recognition
Liming Wang, Mark Hasegawa-Johnson, Chang D. Yoo
Unsupervised speech recognition (ASR-U) is the problem of learning automatic speech recognition (ASR) systems from unpaired speech-only and text-only corpora. While various algorit…