activity
20202022
most citedMulti-Encoder-Decoder Transformer for Code-Switching Speech Recognition

4 citations · 14 across the 7 of their papers we have counts for

collaborators

10 papers

eess.AS20221 cited

Dynamic Acoustic Compensation and Adaptive Focal Training for Personalized Speech Enhancement

Xiaofeng Ge, Jiangyu Han, Haixin Guan +1

Recently, more and more personalized speech enhancement systems (PSE) with excellent performance have been proposed. However, two critical issues still limit the performance and ge…

eess.AS20221 cited

PercepNet+: A Phase and SNR Aware PercepNet for Real-Time Speech Enhancement

Xiaofeng Ge, Jiangyu Han, Yanhua Long +1

PercepNet, a recent extension of the RNNoise, an efficient, high-quality and real-time full-band speech enhancement technique, has shown promising performance in various public dee…

eess.AS2022

Selective Pseudo-labeling and Class-wise Discriminative Fusion for Sound Event Detection

Yunhao Liang, Yanhua Long, Yijie Li +1

In recent years, exploring effective sound separation (SSep) techniques to improve overlapping sound event detection (SED) attracts more and more attention. Creating accurate separ…

eess.AS2022

DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction

Jiangyu Han, Yanhua Long, Lukas Burget +1

In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks.…

eess.AS20213 cited

Improving Channel Decorrelation for Multi-Channel Target Speech Extraction

Jiangyu Han, Wei Rao, Yannan Wang +1

Target speech extraction has attracted widespread attention. When microphone arrays are available, the additional spatial information can be helpful in extracting the target speech…

eess.AS20213 cited

CNN-based Discriminative Training for Domain Compensation in Acoustic Event Detection with Frame-wise Classifier

Tiantian Tang, Xinyuan Zhou, Yanhua Long +2

Domain mismatch is a noteworthy issue in acoustic event detection tasks, as the target domain data is difficult to access in most real applications. In this study, we propose a nov…