Paper Info
Title
Quality measures for speaker verification with short utterances.
Abstract
The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model–universal background model (GMM–UBM) and i-vector. The proposed methods demonstrate considerable improvement in speaker recognition performance on NIST SRE corpora, especially in short duration conditions. We have also observed improvement over existing systems based on different duration-based quality measures.
Year
DOI
Venue
2019
10.1016/j.dsp.2019.01.023
Digital Signal Processing
Keywords
DocType
Volume
Gaussian mixture model (GMM),Identity vector (i-vector),Short utterances,Speaker verification,Total variability,Universal background model (UBM)
Journal
88
ISSN
Citations 
PageRank 
1051-2004
0
0.34
References 
Authors
42
3
Name
Order
Citations
PageRank
Arnab Poddar162.13
Md. Sahidullah232624.99
Goutam Saha325523.17