1 code implementation • 4 Jan 2022 • Luciana Ferrer, Diego Castan, Mitchell McLaren, Aaron Lawson
We show that this hierarchical approach consistently outperforms the non-hierarchical one for detection of highly related languages, in many cases by large margins.
1 code implementation • 2 Feb 2021 • Luciana Ferrer, Mitchell McLaren, Niko Brummer
When trained on a number of diverse datasets that are labeled only with respect to speaker, the proposed backend consistently and, in some cases, dramatically improves calibration, compared to the standard PLDA approach, on a number of held-out datasets, some of which are markedly different from the training data.
no code implementations • 12 Dec 2020 • Arsha Nagrani, Joon Son Chung, Jaesung Huh, Andrew Brown, Ernesto Coto, Weidi Xie, Mitchell McLaren, Douglas A Reynolds, Andrew Zisserman
We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020.
no code implementations • 2 Apr 2020 • Tharindu Fernando, Sridha Sridharan, Mitchell McLaren, Darshana Priyasad, Simon Denman, Clinton Fookes
This paper presents a novel framework for Speech Activity Detection (SAD).
2 code implementations • 5 Feb 2020 • Luciana Ferrer, Mitchell McLaren
In a recent work, we presented a discriminative backend for speaker verification that achieved good out-of-the-box calibration performance on most tested conditions containing varying levels of mismatch to the training conditions.
no code implementations • 5 Dec 2019 • Joon Son Chung, Arsha Nagrani, Ernesto Coto, Weidi Xie, Mitchell McLaren, Douglas A. Reynolds, Andrew Zisserman
The VoxCeleb Speaker Recognition Challenge 2019 aimed to assess how well current speaker recognition technology is able to identify speakers in unconstrained or `in the wild' data.
no code implementations • 26 Nov 2019 • Luciana Ferrer, Mitchell McLaren
However, unlike the standard backends, all parameters of the model are jointly trained to optimize the binary cross-entropy for the speaker verification task.
3 code implementations • 27 Feb 2019 • Mahesh Kumar Nandwana, Julien van Hout, Mitchell McLaren, Colleen Richey, Aaron Lawson, Maria Alejandra Barrios
The "VOiCES from a Distance Challenge 2019" is designed to foster research in the area of speaker recognition and automatic speech recognition (ASR) with the special focus on single channel distant/far-field audio, under noisy conditions.
Audio and Speech Processing Sound
no code implementations • 23 Oct 2018 • Emre Yilmaz, Mitchell McLaren, Henk van den Heuvel, David A. van Leeuwen
In this paper, we describe several automatic annotation approaches to enable using of a large amount of raw bilingual broadcast data for acoustic model training in a semi-supervised setting.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
no code implementations • 28 Mar 2018 • Luciana Ferrer, Mitchell McLaren
The approach does not change the basic form of PLDA but rather modifies the training procedure to consider the dependency across samples of the latent variable that models within-class variability.