An Efficient Learning Based Smartphone Playback Attack Detection Using GMM Supervector
Chun Wang, Yuexian Zou, Shihan Liu, Wei Shi, Weiqiao Zheng · 2016
Playback attack detection (PAD) is essentially a binary classification task which is used to identify the authentic recordings from the playback recordings. For PAD problem, the difference of the acoustic feature between the authentic and playback recordings mainly comes from the recording channel and the ambient noise. Motivated by the excellent performance of the Gaussian Mixture Model-Universal Background Model (GMM-UBM) in modeling the characteristics of speaker and the GMM supervector (GSV) in characterizing speech utterances, this paper proposes an efficient learning based smartphone PAD system using the GSV feature and kernel support vector machine (SVM) (termed as GSV-SVM-PAD). To facilitate the performance of our proposed PAD system, a playback attack detection database (PADD) is designed where more than 14,000 utterances of 100 speakers have been recorded. Extensive experimental results show that the proposed GSV-SVM-PAD system offers a great performance and the lowest number of error classification (NEC) with the increasing tested speakers and utterances. Besides, the NEC of GSV-SVM-PAD is even less than 5 when 7694 utterances of 100 speakers are tested.