Voice-Quality Features for Replay Attack Detection

Abraham Zewoudie, Tom Bäckström

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

4 Citations (Scopus)
125 Downloads (Pure)


Replay attacks are attempts to get fraudulent access to an automatic speaker verification system. In this paper, we investigate the usefulness of voice quality features to detect replay attacks. The voice quality features are used together with the state-of-the-art constant Q cepstral coefficients (CQCC) features. The two feature sets are fused at the score level. Thus, the log-likelihood scores estimated from the two feature sets are linearly weighted to obtain a single fused score. The fused score is used to classify whether a given speech sample is genuine or spoofed. Our experiments with the ASVspoof 2017 dataset demonstrate that the fusion of log-likelihood scores extracted from the CQCC and voice quality features improve the Equal Error Rate (EER) compared to the baseline system which is based only on CQCC features.
Original languageEnglish
Title of host publication2022 30th European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)978-90-827970-9-1
ISBN (Print)978-1-6654-6799-5
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Belgrade, Serbia
Duration: 29 Aug 20222 Sept 2022
Conference number: 30

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491
ISSN (Electronic)2076-1465


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
Internet address


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