3 citations · 5 across the 10 of their papers we have counts for
10 papers
SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings
Priyam Mazumdar, Yurii Halychanskyi, Steven Guo +2
Recent advances in speech synthesis have shifted from phoneme representations to direct grapheme modeling. While phonemes address the one-to-many mapping between text and acoustics…
Accent Conversion: A Problem-Driven Survey of Sociolinguistic and Technical Constraints
Yurii Halychanskyi, Jianfeng Steven Guo, Volodymyr Kindratenko
Accent conversion has rapidly progressed alongside growing interest in improving global cross-cultural communication. This survey presents an overview of the evolution of accent co…
Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?
Yurii Halychanskyi, Nimet Beyza Bozdag, Mark Hasegawa-Johnson +2
Synthetic accented speech is a promising way to improve automatic speech recognition (ASR) when real accented recordings are scarce. We ask what makes such data useful for ASR fine…
FAC-FACodec: Controllable Zero-Shot Foreign Accent Conversion with Factorized Speech Codec
Yurii Halychanskyi, Cameron Churchwell, Yutong Wen +1
Previous accent conversion (AC) methods, including foreign accent conversion (FAC), lack explicit control over the degree of modification. Because accent modification can alter the…
Neural reservoir control of a soft bio-hybrid arm
Noel Naughton, Arman Tekinalp, Keshav Shivam +3
A long-standing engineering problem, the control of soft robots is difficult because of their highly non-linear, heterogeneous, anisotropic, and distributed nature. Here, bridging…
Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks
Ayush Khot, Xiwei Wang, Avik Roy +2
Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In t…
TinyHelen's First Curriculum: Training and Evaluating Tiny Language Models in a Simpler Language Environment
Ke Yang, Volodymyr Kindratenko, ChengXiang Zhai
Training language models (LMs) and their application agents is increasingly costly due to large datasets and models, making test failures difficult to bear. Simplified language env…
ORBIT: Cost-Effective Dataset Curation for Large Language Model Domain Adaptation with an Astronomy Case Study
Eric Modesitt, Ke Yang, Spencer Hulsey +2
Recent advances in language modeling demonstrate the need for high-quality domain-specific training data, especially for tasks that require specialized knowledge. General-purpose m…
RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Boyao Wang, Volodymyr Kindratenko
Convolutional Neural Networks (CNNs) have demonstrated exceptional performance in recent years. Compressing these models not only reduces storage requirements, making deployment to…
AIDOVECL: AI-generated Dataset of Outpainted Vehicles for Eye-level Classification and Localization
Amir Kazemi, Qurat ul ain Fatima, Volodymyr Kindratenko +1
Image labeling is a critical bottleneck in the development of computer vision technologies, often constraining machine learning performance due to the time-intensive nature of manu…