1 citations · 1 across the 7 of their papers we have counts for
7 papers
CosyAccent: Duration-Controllable Accent Normalization Using Source-Synthesis Training Data
Qibing Bai, Shuhao Shi, Shuai Wang +3
Accent normalization (AN) systems often struggle with unnatural outputs and undesired content distortion, stemming from both suboptimal training data and rigid duration modeling. I…
MC-SpEx: Towards Effective Speaker Extraction with Multi-Scale Interfusion and Conditional Speaker Modulation
Jun Chen, Wei Rao, Zilin Wang +5
The previous SpEx+ has yielded outstanding performance in speaker extraction and attracted much attention. However, it still encounters inadequate utilization of multi-scale inform…
TEA-PSE 3.0: Tencent-Ethereal-Audio-Lab Personalized Speech Enhancement System For ICASSP 2023 DNS Challenge
Yukai Ju, Jun Chen, Shimin Zhang +6
This paper introduces the Unbeatable Team's submission to the ICASSP 2023 Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE, to its upgraded version -- T…
Multi-Task Sub-Band Network For Deep Residual Echo Suppression
Jiayao Sun, Dawei Luo, Zhaoxia Li +3
This paper introduces the SWANT team entry to the ICASSP 2023 AEC Challenge. We submit a system that cascades a linear filter with a neural post-filter. Particularly, we adopt sub-…
Speech Enhancement with Intelligent Neural Homomorphic Synthesis
Shulin He, Wei Rao, Jinjiang Liu +5
Most neural network speech enhancement models ignore speech production mathematical models by directly mapping Fourier transform spectrums or waveforms. In this work, we propose a…
Hierarchical speaker representation for target speaker extraction
Shulin He, Huaiwen Zhang, Wei Rao +4
Target speaker extraction aims to isolate a specific speaker's voice from a composite of multiple sound sources, guided by an enrollment utterance or called anchor. Current methods…
Personalized Acoustic Echo Cancellation for Full-duplex Communications
Shimin Zhang, Ziteng Wang, Yukai Ju +4
Deep neural networks (DNNs) have shown promising results for acoustic echo cancellation (AEC). But the DNN-based AEC models let through all near-end speakers including the interfer…