31 citations · 41 across the 7 of their papers we have counts for
7 papers
The FlySpeech Audio-Visual Speaker Diarization System for MISP Challenge 2022
Li Zhang, Huan Zhao, Yue Li +6
This paper describes the FlySpeech speaker diarization system submitted to the second \textbf{M}ultimodal \textbf{I}nformation Based \textbf{S}peech \textbf{P}rocessing~(\textbf{MI…
Wespeaker baselines for VoxSRC2023
Shuai Wang, Chengdong Liang, Xu Xiang +4
This report showcases the results achieved using the wespeaker toolkit for the VoxSRC2023 Challenge. Our aim is to provide participants, especially those with limited experience, w…
Distance-based Weight Transfer from Near-field to Far-field Speaker Verification
Li Zhang, Qing Wang, Hongji Wang +4
The scarcity of labeled far-field speech is a constraint for training superior far-field speaker verification systems. Fine-tuning the model pre-trained on large-scale near-field s…
A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation
Chulun Zhou, Yunlong Liang, Fandong Meng +5
Neural chat translation (NCT) aims to translate a cross-lingual chat between speakers of different languages. Existing context-aware NMT models cannot achieve satisfactory performa…
Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit
Hongji Wang, Chengdong Liang, Shuai Wang +5
Speaker modeling is essential for many related tasks, such as speaker recognition and speaker diarization. The dominant modeling approach is fixed-dimensional vector representation…
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation
Chulun Zhou, Fandong Meng, Jie Zhou +3
Most dominant neural machine translation (NMT) models are restricted to make predictions only according to the local context of preceding words in a left-to-right manner. Although…
Asynchronous Bidirectional Decoding for Neural Machine Translation
Xiangwen Zhang, Jinsong Su, Yue Qin +3
The dominant neural machine translation (NMT) models apply unified attentional encoder-decoder neural networks for translation. Traditionally, the NMT decoders adopt recurrent neur…