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Hyewon Han

2 accepted papers

2025

StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models

ICASSP 2025accepted

In this paper, we propose StableQuant, a novel adaptive post-training quantization (PTQ) algorithm for widely used speech foundation models (SFMs). While PTQ has been successfully employed for compressing large language models (LLMs) due to its ability to bypass additional fine-tuning, directly appl…

Cited by 0SourceScholar
2022

Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement

ICASSP 2022accepted

Modern neural speech enhancement models usually include various forms of phase information in their training loss terms, either explicitly or implicitly. However, these loss terms are typically designed to reduce the distortion of phase spectrum values at specific frequencies, which ensures they do…

Cited by 0SourceScholar