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Xuyi Zhuang

5 accepted papers

2026

ALIGNING GENERATIVE SPEECH ENHANCEMENT WITH PERCEPTUAL FEEDBACK

ICASSP 2026oral

Language Model (LM)-based speech enhancement (SE) has recently emerged as a promising direction, but existing approaches predominantly rely on token-level likelihood objectives that weakly reflect human perception. This mismatch limits progress, as optimizing signal accuracy does not always improve…

Cited by 0SourcePDFScholar
2025

Joint Training Framework for Accent and Speech Recognition Based on Conformer Low-Rank Adaptation

ICASSP 2025accepted

In real-world scenarios, accent variations often reduce Automatic Speech Recognition (ASR) accuracy. Addressing this typically involves a multi-task ASR and Accent Recognition (ASR-AR) framework, but there is limited research on optimizing task-specific feature extraction and enhancing ASR with AR i…

Cited by 0SourceScholar
2023

Half-Temporal and Half-Frequency Attention U2Net for Speech Signal Improvement

ICASSP 2023accepted

During communication, volume changes, noise, and reverberation can disturb speech signals, significantly affecting the quality and intelligibility of speech. In the context of the ICASSP 2023 Signal Processing Grand Challenge, the first Speech Signal Improvement Grand Challenge (SIG) is organized to…

Cited by 0SourceScholar
2023

Two-Stage UNet with Multi-Axis Gated Multilayer Perceptron for Monaural Noisy-Reverberant Speech Enhancement

ICASSP 2023accepted

In denoising and de-reverberation tasks, the dominant methods are complex spectral masking and complex spectral mapping. To combine advantages and improve speech enhancement performance, we propose a two-stage UNet (TSUNet) to estimate complex spectral masking and complex spectral mapping. We use a…

Cited by 0SourceScholar
2022

FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on Multi-Scale Temporal Convolutional Network

ICASSP 2022accepted

In recent years, deep learning-based approaches have significantly improved the performance of single-channel speech enhancement. However, due to the limitation of training data and computational complexity, real-time enhancement of full-band (48 kHz) speech signals is still very challenging. Becaus…

Cited by 0SourceScholar