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Spectral Root Features for Replay Spoof Detection in Voice Assistants

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Speech and Computer (SPECOM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12997))

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Abstract

In this paper, authors propose spectral root cepstral coefficients (SRCC) feature set to develop the effective countermeasure system for replay attacks on voice assistants (VAs). Experiments are performed on ReMASC dataset, which is specifically designed for the replay attack detection task. Logarithm operation in MFCC extraction is replaced by power-law nonlinearity (i.e. \((\cdot )^\gamma \)) to derive SRCC feature set. The proper choice of the \(\gamma \) helps to capture the system information of the speech signal, with a minimum number of cepstral coefficients. We investigated two approaches for proper choice of \(\gamma \)-value, in particular, by estimating the energy concentration in cepstral coefficients and by visualizing the spectrogram w.r.t. \(\gamma \)-value. This system representation of the speech signal, is the discriminative cue for the replay spoof speech detection (SSD) task as replay speech signal consists of additional transmission channel effects convolved with the genuine signal. The performance of the proposed feature set is validated using Gaussian Mixture Model (GMM), and Light Convolutional Neural Network (LCNN). Our primary system shows relative improvement of 47.49% over the baseline system (Constant-Q Cepstral Coefficients (CQCC)-GMM) on the evaluation set. The EER is further reduced to 11.84% on evaluation set by classifier-level fusion.

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Authors and Affiliations

  1. Speech Research Lab, Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, 382007, India

    Ankur T. Patil, Harsh Kotta, Rajul Acharya & Hemant A. Patil

Authors
  1. Ankur T. Patil
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  2. Harsh Kotta
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  3. Rajul Acharya
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  4. Hemant A. Patil
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Correspondence to Ankur T. Patil .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Patil, A.T., Kotta, H., Acharya, R., Patil, H.A. (2021). Spectral Root Features for Replay Spoof Detection in Voice Assistants. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_46

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  • DOI: https://doi.org/10.1007/978-3-030-87802-3_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87801-6

  • Online ISBN: 978-3-030-87802-3

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