Search Results for author: Douglas Reynolds

Found 5 papers, 1 papers with code

The 2022 NIST Language Recognition Evaluation

no code implementations28 Feb 2023 Yooyoung Lee, Craig Greenberg, Eliot Godard, Asad A. Butt, Elliot Singer, Trang Nguyen, Lisa Mason, Douglas Reynolds

In 2022, the U. S. National Institute of Standards and Technology (NIST) conducted the latest Language Recognition Evaluation (LRE) in an ongoing series administered by NIST since 1996 to foster research in language recognition and to measure state-of-the-art technology.

valid

The 2021 NIST Speaker Recognition Evaluation

no code implementations21 Apr 2022 Seyed Omid Sadjadi, Craig Greenberg, Elliot Singer, Lisa Mason, Douglas Reynolds

Evaluation results indicate: audio-visual fusion produce substantial gains in performance over audio-only or visual-only systems; top performing speaker and face recognition systems exhibited comparable performance under the matched domain conditions present in this evaluation; and, the use of complex neural network architectures (e. g., ResNet) along with angular losses with margin, data augmentation, as well as long duration fine-tuning contributed to notable performance improvements for the audio-only speaker recognition task.

Data Augmentation Face Recognition +2

The NIST CTS Speaker Recognition Challenge

no code implementations21 Apr 2022 Seyed Omid Sadjadi, Craig Greenberg, Elliot Singer, Lisa Mason, Douglas Reynolds

The US National Institute of Standards and Technology (NIST) has been conducting a second iteration of the CTS challenge since August 2020.

Data Augmentation Speaker Recognition

MCE 2018: The 1st Multi-target Speaker Detection and Identification Challenge Evaluation (MCE) Plan, Dataset and Baseline System

1 code implementation17 Jul 2018 Suwon Shon, Najim Dehak, Douglas Reynolds, James Glass

The Multitarget Challenge aims to assess how well current speech technology is able to determine whether or not a recorded utterance was spoken by one of a large number of 'blacklisted' speakers.

Audio and Speech Processing Sound

A Unified Deep Neural Network for Speaker and Language Recognition

no code implementations3 Apr 2015 Fred Richardson, Douglas Reynolds, Najim Dehak

Learned feature representations and sub-phoneme posteriors from Deep Neural Networks (DNNs) have been used separately to produce significant performance gains for speaker and language recognition tasks.

Domain Adaptation Speaker Recognition

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