Database dependence comparison in detection of physical access voice spoofing attacks
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AuthorChica Villar, Manuel; Gómez Alanís, Alejandro; Roselló Casado, Eros; Gómez García, Ángel Manuel; Peinado Herreros, Antonio Miguel; González López, José Andrés
ISCA - Iberspeech 2022
Spoofing detectionDeep learningAntispoofingSpeaker verification
SponsorshipProject PID2019-104206GB-I00 funded by MCIN/AEI/10.13039/501100011033; FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades. Proyecto PY20_00902
The antispoofing challenges are designed to work on a sin- gle database, on which we can test our model. The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative that aims to promote the consideration of spoofing and the development of countermeasures. In general, the idea of analyzing the databases individually has been the domain approach but this could be rather misleading. This paper provides a study of the general- ization capability of antispoofing systems based on neural net- works by combining different databases for training and testing. We will try to give a broader vision of the advantages of group- ing different datasets. We will delve into the ”replay attacks” on physical data. This type of attack is one of the most difficult to detect since only a few minutes of audio samples are needed to impersonate the voice of a genuine speaker and gain access to the ASV system. To carry out this task, the ASV databases from ASVspoof-challenge have been chosen and will be used to have a more concrete and accurate vision of them. We report results on these databases using different neural network architectures and set-ups.