Cross-Teager Energy Cepstral Coefficients for Replay Spoof Detection on Voice Assistants
Rajul Acharya, Harsh Kotta, Ankur T. Patil, Hemant A. Patil
Abstract
Voice assistants (VAs) are highly vulnerable to replay attacks, where the impostor plays pre-recorded voice samples to gain an unauthorized access to personalised devices. To that effect, we present an optimal microphone-channel selection scheme using Cross-Teager Energy Operator (CTEO) for spoofed speech detection (SSD) task. Here, a channel refers to the speech signal obtained from a single microphone among the microphone array. The key idea of this work is optimal channel selection based on maximum cross-energies from a multichannel input, which is suitable for SSD task. This newly proposed feature set is named as Cross-Teager Energy Cepstal Coefficients (CTECC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> ). The reason be hind maximizing the cross-energies is to identify the distortions in replay speech signal which is added due to intermediate devices. This key idea is also cross-validated by selecting the least estimated cross-energies as feature set CTECC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> . The noticeable improvement in the performance is observed for CTECC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</inf> over CTECC <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</inf> for two classifiers, namely, Gaussian Mixture Model (GMM) and Light Convolutional Neural Network (LCNN).
BibTeX
@inproceedings{icassp2021_crossteagerenerg,
title = {Cross-Teager Energy Cepstral Coefficients for Replay Spoof Detection on Voice Assistants},
author = {Rajul Acharya and Harsh Kotta and Ankur T. Patil and Hemant A. Patil},
booktitle = {ICASSP 2021},
year = {2021}
}