no code implementations • 28 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.
no code implementations • 21 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.
no code implementations • 21 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.
1 code implementation • 17 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
no code implementations • 3 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.