Paper Info
Title
Improving Speaker Verification Performance In Presence Of Spoofing Attacks Using Out-Of-Domain Spoofed Data
Abstract
Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks using speech generated by voice conversion and speech synthesis techniques. Commonly, a countermeasure (CM) system is integrated with an ASV system for improved protection against spoofing attacks. But integration of the two systems is challenging and often leads to increased false rejection rates. Furthermore, the performance of CM severely degrades if in-domain development data are unavailable. In this study, therefore. we propose a solution that uses two separate background models - one from human speech and another from spoofed data. During test, the ASV score for an input utterance is computed as the difference of the log-likelihood against the target model and the combination of the log-likelihoods against two background models. Evaluation experiments are conducted using the joint ASV and CM protocol of ASV spoof 2015 corpus consisting of text-independent ASV tasks with short utterances. Our proposed system reduces error rates in the presence of spoofing attacks by using out-of-domain spoofed data for system development, while maintaining the performance for zero-effort imposter attacks compared to the baseline system.
Year
DOI
Venue
2017
10.21437/Interspeech.2017-1758
18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), VOLS 1-6: SITUATED INTERACTION
Keywords
Field
DocType
Speaker verification, Spoofing, UBM, Cross-corpora
Speaker verification,Spoofing attack,Computer science,Speech recognition
Conference
ISSN
Citations 
PageRank 
2308-457X
1
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Achintya Kumar Sarkar1237.81
Md. Sahidullah232624.99
Zheng-Hua Tan345760.32
Tomi Kinnunen4132386.67