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Noise Robust ASV Spoof Detection Using Integrated Features and Time Delay Neural Network

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Abstract

Recent advancements in research for development of countermeasure systems for Spoofed Audio detection has helped in building more robust Automatic Speaker Verification (ASV) System. However, available countermeasure systems are not able to generalize well against unknown attacks. The lack of context-dependent information extracted from the given speech at fine grained level is the dominating reason for poor performance of these systems against unknown attacks. To build a noise robust anti-spoof system, in this paper, we propose a Time Delay Neural Network (TDNN)-based countermeasure system that captures context-dependent information well. We devise a three-stage design where at first audio is pre-processed to extract useful information using three different types of features, that are, Mel Frequency Cepstral Coefficients (MFCC), noise robust Gammatone Cepstral Coefficients (GTCC) features and integration of MFCC-GTCC features. These features are then input to proposed Deep Neural Network (DNN) model that uses Long Short-Term Memory (LSTM) network for recurrent aggregation of layer wise generated shallow features in TDNN. Finally, the output is passed through context-dependent pooling layer to generate fixed-length representation that is further used at third stage to classify speech as genuine or spoofed. The proposed system is tested on Logical Access (LA) track of ASV Spoof 2019 dataset, and achieves performance improvement of about 59.7% and 65.9% relative to earlier proposed Linear-Frequency Cepstral Coefficients-Gaussian Mixture Model (LFCC-GMM) and Constant Q Cepstral Coefficients-Gaussian Mixture Model (CQCC-GMM) baseline models, respectively.

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All the data generated or analyzed during this study are included and referred to in this published article.

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Funding

I, Dr. Mohit Dua, on the behalf of all the authors declare that this study did not receive any funding from any resource.

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  1. Department of Computer Engineering, National Institute of Technology, Kurukshetra, India

    Nidhi Chakravarty & Mohit Dua

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  1. Nidhi Chakravarty
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Correspondence to Nidhi Chakravarty.

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This article is part of the topical collection “Enabling Innovative Computational Intelligence Technologies for IOT” guest edited by Omer Rana, Rajiv Misra, Alexander Pfeiffer, Luigi Troiano and Nishtha Kesswani.

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Chakravarty, N., Dua, M. Noise Robust ASV Spoof Detection Using Integrated Features and Time Delay Neural Network. SN COMPUT. SCI. 4, 127 (2023). https://doi.org/10.1007/s42979-022-01557-4

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