BUT reports x-vector systems achieving 1.0% EER via fusion on the fixed condition of the VOiCES 2019 speaker recognition challenge.
The VOiCES from a Distance Challenge 2019 Evaluation Plan
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
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. The main objectives of this challenge are to: (i) benchmark state-of-the-art technology in the area of speaker recognition and automatic speech recognition (ASR), (ii) support the development of new ideas and technologies in speaker recognition and ASR, (iii) support new research groups entering the field of distant/far-field speech processing, and (iv) provide a new, publicly available dataset to the community that exhibits realistic distance characteristics.
fields
eess.AS 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
DKU's ResNet-based system with angular softmax and WPE achieves 0.3532 minDCF and 4.96% EER on the VOiCES 2019 evaluation set using cosine scoring.
citing papers explorer
-
BUT VOiCES 2019 System Description
BUT reports x-vector systems achieving 1.0% EER via fusion on the fixed condition of the VOiCES 2019 speaker recognition challenge.
-
The DKU System for the Speaker Recognition Task of the 2019 VOiCES from a Distance Challenge
DKU's ResNet-based system with angular softmax and WPE achieves 0.3532 minDCF and 4.96% EER on the VOiCES 2019 evaluation set using cosine scoring.