1 citations · 2 across the 11 of their papers we have counts for
12 papers
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…
The Interspeech 2025 Speech Accessibility Project Challenge
Xiuwen Zheng, Bornali Phukon, Jonghwan Na +13
While the last decade has witnessed significant advancements in Automatic Speech Recognition (ASR) systems, performance of these systems for individuals with speech disabilities re…
Towards Unsupervised Speech Recognition Without Pronunciation Models
Junrui Ni, Liming Wang, Yang Zhang +4
Recent advancements in supervised automatic speech recognition (ASR) have achieved remarkable performance, largely due to the growing availability of large transcribed speech corpo…
Unsupervised Speech Recognition with N-Skipgram and Positional Unigram Matching
Liming Wang, Mark Hasegawa-Johnson, Chang D. Yoo
Training unsupervised speech recognition systems presents challenges due to GAN-associated instability, misalignment between speech and text, and significant memory demands. To tac…
Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction
Eunseop Yoon, Hee Suk Yoon, Dhananjaya Gowda +7
Text-to-Text Transfer Transformer (T5) has recently been considered for the Grapheme-to-Phoneme (G2P) transduction. As a follow-up, a tokenizer-free byte-level model based on T5 re…
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…
INTapt: Information-Theoretic Adversarial Prompt Tuning for Enhanced Non-Native Speech Recognition
Eunseop Yoon, Hee Suk Yoon, John Harvill +2
Automatic Speech Recognition (ASR) systems have attained unprecedented performance with large speech models pre-trained based on self-supervised speech representation learning. How…
SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation
Hee Suk Yoon, Eunseop Yoon, John Harvill +3
Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. Although current systems have attained un…
Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold
Junghyun Lee, Gwangsu Kim, Matt Olfat +2
This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different pr…