Speaker recognition using common passphrases in RedDots

In this paper we report our work on the recently collected text dependent speaker recognition dataset named RedDots, with a focus on the common passphrase condition. We first investigate an out-of-the-box approach. We then report several strategies to train on RedDots itself using up to 40 speakers...

Full description

Saved in:
Bibliographic Details
Published in2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5405 - 5409
Main Author Aronowitz, Hagai
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2017
Subjects
Aging
Authentication
Bagging
Covariance matrices
pass-phrase quality estimation
Protocols
RedDots
Speaker recognition
template aging
text dependent speaker recognition
Training
Online AccessGet full text

Cover

More Information
Summary:In this paper we report our work on the recently collected text dependent speaker recognition dataset named RedDots, with a focus on the common passphrase condition. We first investigate an out-of-the-box approach. We then report several strategies to train on RedDots itself using up to 40 speakers for training. The GMM-NAP framework is used as a baseline. We report the following novelties: First, we demonstrate the use of bagging for improved accuracy. Second, we estimate the EER of a passphrase using metadata only. Third, the estimated EERs are used for improved score normalization. Finally we report an analysis of system sensitivity to the duration between enrollment and testing (template aging).
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7953189