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Found 10 papers, 4 papers with code
A Discriminative Hierarchical PLDA-based Model for Spoken Language Recognition
We show that this hierarchical approach consistently outperforms the non-hierarchical one for detection of highly related languages, in many cases by large margins.
A Speaker Verification Backend with Robust Performance across Conditions
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.
VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge
We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020.
Temporarily-Aware Context Modelling using Generative Adversarial Networks for Speech Activity Detection
This paper presents a novel framework for Speech Activity Detection (SAD).
A Speaker Verification Backend for Improved Calibration Performance across Varying Conditions
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.
VoxSRC 2019: The first VoxCeleb Speaker Recognition Challenge
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.
A discriminative condition-aware backend for speaker verification
However, unlike the standard backends, all parameters of the model are jointly trained to optimize the binary cross-entropy for the speaker verification task.
The VOiCES from a Distance Challenge 2019 Evaluation Plan
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
Semi-supervised acoustic model training for speech with code-switching
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
Joint PLDA for Simultaneous Modeling of Two Factors
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.
