29 citations · 29 across the 3 of their papers we have counts for
11 papers
LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech Recognition
Eunseop Yoon, Hee Suk Yoon, John Harvill +2
Test-Time Adaptation (TTA) has emerged as a crucial solution to the domain shift challenge, wherein the target environment diverges from the original training environment. A prime…
Sound Tagging in Infant-centric Home Soundscapes
Mohammad Nur Hossain Khan, Jialu Li, Nancy L. McElwain +2
Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic envi…
C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee +3
In deep learning, test-time adaptation has gained attention as a method for model fine-tuning without the need for labeled data. A prime exemplification is the recently proposed te…
AdaMER-CTC: Connectionist Temporal Classification with Adaptive Maximum Entropy Regularization for Automatic Speech Recognition
SooHwan Eom, Eunseop Yoon, Hee Suk Yoon +3
In Automatic Speech Recognition (ASR) systems, a recurring obstacle is the generation of narrowly focused output distributions. This phenomenon emerges as a side effect of Connecti…
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…