End-to-end spoofing detection with raw waveform CLDNNs

Abstract

Albeit recent progress in speaker verification generates powerful models, malicious attacks in the form of spoofed speech, are generally not coped with. Recent results in ASVSpoof2015 and BTAS2016 challenges indicate that spoof-aware features are a possible solution to this problem. Most successful methods in both challenges focus on spoof-aware features, rather than focusing on a powerful classifier. In this paper we present a novel raw waveform based deep model for spoofing detection, which jointly acts as a feature extractor and classifier, thus allowing it to directly classify speech signals. This approach can be considered as an end-to-end classifier, which removes the need for any pre- or post-processing on the data, making training and evaluation a streamlined process, consuming less time than other neural-network based approaches. The experiments on the BTAS2016 dataset show that the system performance is significantly improved by the proposed raw waveform convolutional long short term neural network (CLDNN), from the previous best published 1.26\% half total error rate (HTER) to the current 0.82\% HTER. Moreover it shows that the proposed system also performs well under the unknown (RE-PH2-PH3,RE-LPPH2-PH3) conditions.

5 pages

References in corpus (1)

• ADADELTA: An Adaptive Learning Rate Method

Cited by in corpus (10)

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• Open Challenges in Synthetic Speech Detection
• Speech and Speaker Recognition from Raw Waveform with SincNet
• RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
• Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examples
• A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection
• Detection of Glottal Closure Instants from Raw Speech using Convolutional Neural Networks
• Deep Generative Variational Autoencoding for Replay Spoof Detection in Automatic Speaker Verification
• FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning
• Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection