Neural Kalman Filtering for Speech Enhancement

no code implementations • 28 Jul 2020 • Wei Xue, Gang Quan, Chao Zhang, Guohong Ding, Xiaodong He, BoWen Zhou

Statistical signal processing based speech enhancement methods adopt expert knowledge to design the statistical models and linear filters, which is complementary to the deep neural network (DNN) based methods which are data-driven.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences

no code implementations • 16 Apr 2019 • Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen, Hitoshi Yamamoto, Koji Okabe, Ville Vestman, Jing Huang, Guohong Ding, Hanwu Sun, Anthony Larcher, Rohan Kumar Das, Haizhou Li, Mickael Rouvier, Pierre-Michel Bousquet, Wei Rao, Qing Wang, Chunlei Zhang, Fahimeh Bahmaninezhad, Hector Delgado, Jose Patino, Qiongqiong Wang, Ling Guo, Takafumi Koshinaka, Jiacen Zhang, Koichi Shinoda, Trung Ngo Trong, Md Sahidullah, Fan Lu, Yun Tang, Ming Tu, Kah Kuan Teh, Huy Dat Tran, Kuruvachan K. George, Ivan Kukanov, Florent Desnous, Jichen Yang, Emre Yilmaz, Longting Xu, Jean-Francois Bonastre, Cheng-Lin Xu, Zhi Hao Lim, Eng Siong Chng, Shivesh Ranjan, John H. L. Hansen, Massimiliano Todisco, Nicholas Evans

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).

Domain Adaptation Speaker Recognition

Deep Speaker Embedding Learning with Multi-Level Pooling for Text-Independent Speaker Verification

no code implementations • 21 Feb 2019 • Yun Tang, Guohong Ding, Jing Huang, Xiaodong He, Bo-Wen Zhou

This paper aims to improve the widely used deep speaker embedding x-vector model.

Text-Independent Speaker Verification