Voice liveness detection using phoneme-based pop-noise detector for speaker verification

Shihono Mochizuki, Sayaka Shiota, Hitoshi Kiya


This paper proposes a phoneme-based pop-noise (PN) detection algorithm for voice liveness detection (VLD) and automatic speaker verification systems. Recently, a lot of countermeasures against spoofing attacks (e.g., replay, speech synthesis) have been reported for speaker verification systems. A principle mechanism of almost all spoofing attacks is to replay recorded speeches via a loudspeaker. Therefore, one of the effective solutions against spoofing attacks is to determine whether an input speech is a genuine voice or a replayed one, and this is a framework of VLD. To realize the VLD framework, PN detection methods have been proposed. Since PN is a common distortion that occurs when speaker's breath reaches the inside of a microphone, the conventional PN detection methods simply capture PN periods during the input speech. However, the performances of the PN detection methods depend on microphone types and phrases. It may lead to vulnerability of the conventional PN detection methods. This paper proposes a novel PN detection method, focused on specific characteristics of phonemes related to the PN phenomenon. The experimental results show that the proposed method provides a higher performance than conventional PN detection methods.


 DOI: 10.21437/Odyssey.2018-33

Cite as: Mochizuki, S., Shiota, S., Kiya, H. (2018) Voice liveness detection using phoneme-based pop-noise detector for speaker verification . Proc. Odyssey 2018 The Speaker and Language Recognition Workshop, 233-239, DOI: 10.21437/Odyssey.2018-33.


@inproceedings{Mochizuki2018,
  author={Shihono Mochizuki and Sayaka Shiota and Hitoshi Kiya},
  title={Voice liveness detection using phoneme-based pop-noise detector for speaker verification	},
  year=2018,
  booktitle={Proc. Odyssey 2018 The Speaker and Language Recognition Workshop},
  pages={233--239},
  doi={10.21437/Odyssey.2018-33},
  url={http://dx.doi.org/10.21437/Odyssey.2018-33}
}