9 citations · 20 across the 9 of their papers we have counts for
7 papers · 1 filter
DM-ASR: Diarization-aware Multi-speaker ASR with Large Language Models
Li Li, Ming Cheng, Weixin Zhu +3
Multi-speaker automatic speech recognition (ASR) aims to transcribe conversational speech involving multiple speakers, requiring the model to capture not only what was said, but al…
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…
Joint Training or Not: An Exploration of Pre-trained Speech Models in Audio-Visual Speaker Diarization
Huan Zhao, Li Zhang, Yue Li +5
The scarcity of labeled audio-visual datasets is a constraint for training superior audio-visual speaker diarization systems. To improve the performance of audio-visual speaker dia…
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…
spatial-dccrn: dccrn equipped with frame-level angle feature and hybrid filtering for multi-channel speech enhancement
Shubo Lv, Yihui Fu, Yukai Jv +4
Recently, multi-channel speech enhancement has drawn much interest due to the use of spatial information to distinguish target speech from interfering signal. To make full use of s…
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…