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20212023
most citedMulti-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SD20231 cited

PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification

Zhenduo Zhao, Zhuo Li, Wenchao Wang +1

ECAPA-TDNN is currently the most popular TDNN-series model for speaker verification, which refreshed the state-of-the-art(SOTA) performance of TDNN models. However, one-dimensional…

cs.CL20231 cited

Speech Corpora Divergence Based Unsupervised Data Selection for ASR

Changfeng Gao, Gaofeng Cheng, Pengyuan Zhang +1

Selecting application scenarios matching data is important for the automatic speech recognition (ASR) training, but it is difficult to measure the matching degree of the training c…

eess.AS20231 cited

Multi-dimensional frequency dynamic convolution with confident mean teacher for sound event detection

Shengchang Xiao, Xueshuai Zhang, Pengyuan Zhang

Recently, convolutional neural networks (CNNs) have been widely used in sound event detection (SED). However, traditional convolution is deficient in learning time-frequency domain…

cs.CL2022

The Conversational Short-phrase Speaker Diarization (CSSD) Task: Dataset, Evaluation Metric and Baselines

Gaofeng Cheng, Yifan Chen, Runyan Yang +9

The conversation scenario is one of the most important and most challenging scenarios for speech processing technologies because people in conversation respond to each other in a c…

eess.AS2022

SASV Based on Pre-trained ASV System and Integrated Scoring Module

Yuxiang Zhang, Zhuo Li, Wenchao Wang +1

Based on the assumption that there is a correlation between anti-spoofing and speaker verification, a Total-Divide-Total integrated Spoofing-Aware Speaker Verification (SASV) syste…

eess.AS2022

Interrelate Training and Searching: A Unified Online Clustering Framework for Speaker Diarization

Yifan Chen, Yifan Guo, Qingxuan Li +3

For online speaker diarization, samples arrive incrementally, and the overall distribution of the samples is invisible. Moreover, in most existing clustering-based methods, the tra…