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20212026
most citedMultitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech

11 citations · 27 across the 11 of their papers we have counts for

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eess.AS2026

Evaluating the Expressive Appropriateness of Speech in Rich Contexts

Tianrui Wang, Ziyang Ma, Yizhou Peng +26

Evaluating expressive speech remains challenging, as existing methods mainly assess emotional intensity and overlook whether a speech sample is expressively appropriate for its con…

eess.AS2025

Aligning Generative Speech Enhancement with Perceptual Feedback

Haoyang Li, Nana Hou, Yuchen Hu +6

Language Model (LM)-based speech enhancement (SE) has recently emerged as a promising direction, but existing approaches predominantly rely on token-level likelihood objectives tha…

eess.AS2022★ 1 cited

Dual-Path Style Learning for End-to-End Noise-Robust Speech Recognition

Yuchen Hu, Nana Hou, Chen Chen +1

Automatic speech recognition (ASR) systems degrade significantly under noisy conditions. Recently, speech enhancement (SE) is introduced as front-end to reduce noise for ASR, but i…

eess.AS2021★ 5 cited

Interactive Feature Fusion for End-to-End Noise-Robust Speech Recognition

Yuchen Hu, Nana Hou, Chen Chen +1

Speech enhancement (SE) aims to suppress the additive noise from a noisy speech signal to improve the speech's perceptual quality and intelligibility. However, the over-suppression…

eess.AS2021★ 11 cited

Multitask-Based Joint Learning Approach To Robust ASR For Radio Communication Speech

Duo Ma, Nana Hou, Van Tung Pham +2

To realize robust end-to-end Automatic Speech Recognition(E2E ASR) under radio communication condition, we propose a multitask-based method to joint train a Speech Enhancement (SE)…