2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

Technical Program

Paper Detail

Paper IDSPE-20.3
Paper Title ASV-SUBTOOLS: OPEN SOURCE TOOLKIT FOR AUTOMATIC SPEAKER VERIFICATION
Authors Fuchuan Tong, Miao Zhao, Jianfeng Zhou, Hao Lu, Zheng Li, Lin Li, Qingyang Hong, Xiamen University, China
SessionSPE-20: Speaker Recognition 4: Applications
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Speech Processing: [SPE-SPKR] Speaker Recognition and Characterization
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Virtual Presentation  Click here to watch in the Virtual Conference
Abstract In this paper, we introduce a new open source toolkit for automatic speaker verification (ASV), named ASV-Subtools. Adopting PyTorch as main deep learning engine and Kaldi toolkit for data processing, ASV-Subtools allows users to develop modern speaker recognizers flexibly and efficiently. The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting the state-of-the-art approaches in speaker recognition. In addition to including the commonly used networks, such as the time delay neural networks (TDNN), factorized TDNN (F-TDNN) and ResNet, ASV-Subtools also integrates an upgraded version of SpecAugment data augmentation method, named Inverted SpecAugment, with focus on making it more appropriate for speaker recognition subtasks. Besides, for alleviating the domain mismatch between training and test data, ASV-Subtools provides multiple domain adaptation methods of Probabilistic Linear Discriminant Analysis (PLDA). Experimental results show that state-of-the-art techniques implemented on ASV-Subtools could achieve competitive performance compared to other implementations.