UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation

no code implementations • 24 Oct 2016 • Chunlei Zhang, Fahimeh Bahmaninezhad, Shivesh Ranjan, Chengzhu Yu, Navid Shokouhi, John H. L. Hansen

This document briefly describes the systems submitted by the Center for Robust Speech Systems (CRSS) from The University of Texas at Dallas (UTD) to the 2016 National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE).

Clustering Dimensionality Reduction +1

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

A comprehensive study of speech separation: spectrogram vs waveform separation

no code implementations • 17 May 2019 • Fahimeh Bahmaninezhad, Jian Wu, Rongzhi Gu, Shi-Xiong Zhang, Yong Xu, Meng Yu, Dong Yu

We study the speech separation problem for far-field data (more similar to naturalistic audio streams) and develop multi-channel solutions for both frequency and time-domain separators with utilizing spectral, spatial and speaker location information.

speech-recognition Speech Recognition +1

A Unified Framework for Speech Separation

no code implementations • 17 Dec 2019 • Fahimeh Bahmaninezhad, Shi-Xiong Zhang, Yong Xu, Meng Yu, John H. L. Hansen, Dong Yu

The initial solutions introduced for deep learning based speech separation analyzed the speech signals into time-frequency domain with STFT; and then encoded mixed signals were fed into a deep neural network based separator.

Speech Separation

Bilingual Streaming ASR with Grapheme units and Auxiliary Monolingual Loss

no code implementations • 11 Aug 2023 • Mohammad Soleymanpour, Mahmoud Al Ismail, Fahimeh Bahmaninezhad, Kshitiz Kumar, Jian Wu

Our key developments constitute: (a) pronunciation lexicon with grapheme units instead of phone units, (b) a fully bilingual alignment model and subsequently bilingual streaming transformer model, (c) a parallel encoder structure with language identification (LID) loss, (d) parallel encoder with an auxiliary loss for monolingual projections.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2