Generalization of Audio Deepfake Detection

@inproceedings{Chen2020GeneralizationOA,
  title={Generalization of Audio Deepfake Detection},
  author={Tianxiang Chen and Avrosh Kumar and Parav Nagarsheth and Ganesh Sivaraman and Elie el Khoury},
  booktitle={Odyssey},
  year={2020}
}
Audio Deepfakes, technically known as logical-access voice spoofing techniques, have become an increased threat on voice interfaces due to the recent breakthroughs in speech synthesis and voice conversion technologies. Effectively detecting these attacks is critical to many speech applications including automatic speaker verification systems. As new types of speech synthesis and voice conversion techniques are emerging rapidly, the generalization ability of spoofing countermeasures is becoming… 

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References

SHOWING 1-10 OF 29 REFERENCES

Deep Feature Engineering for Noise Robust Spoofing Detection

TLDR
This paper employs deep feedforward, recurrent, and convolutional neural networks to extract robust and discriminative deep features by using deep learning techniques for spoofing detection and introduces multicondition training, noise-aware training, and annealed dropout training to make neural networks more robust against noise and to avoid overfitting to specific spoofing attacks and noise types.

Long Range Acoustic and Deep Features Perspective on ASVspoof 2019

TLDR
A comprehensive analysis on the nature of different kinds of spoofing attacks and system development is made and the use of deep features that enhances the discriminative ability between genuine and spoofed speech is investigated.

ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection

TLDR
The 2019 database, protocols and challenge results are described, and major findings which demonstrate the real progress made in protecting against the threat of spoofing and fake audio are outlined.

A Spoofing Benchmark for the 2018 Voice Conversion Challenge: Leveraging from Spoofing Countermeasures for Speech Artifact Assessment

TLDR
The preliminary findings suggest potential of CMs outside of their original application, as a supplemental optimization and benchmarking tool to enhance VC technology.

Toward Robust Audio Spoofing Detection: A Detailed Comparison of Traditional and Learned Features

TLDR
This research examines robust audio features, both traditional and those learned through an autoencoder, which is generalizable to different types of replay spoofing, and base the system on a traditional Gaussian mixture model-universal background model (GMM-UBM).

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

TLDR
This work presents SpecAugment, a simple data augmentation method for speech recognition that is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients) and achieves state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work.

Detecting Converted Speech and Natural Speech for anti-Spoofing Attack in Speaker Recognition

TLDR
Experiments show that the performance of the features derived from phase spectrum outperform the melfrequency cepstral coefficients (MFCCs) tremendously: even without converted speech for training, the equal error rate (EER) is reduced from 20.20% of MFCCs to 2.35%.

A comparison of features for synthetic speech detection

TLDR
Comparative results indicate that features representing spectral information in high-frequency region, dynamic information of speech, and detailed information related to subband characteristics are considerably more useful in detecting synthetic speech detection task.

The SJTU Robust Anti-Spoofing System for the ASVspoof 2019 Challenge

TLDR
The SJTU’s submitted antispoofing system shows consistent performance improvement over all types of spoofing attacks and Log-CQT features are developed in conjunction with multi-layer convolutional neural networks for robust performance across both subtasks.

A study on data augmentation of reverberant speech for robust speech recognition

TLDR
It is found that the performance gap between using simulated and real RIRs can be eliminated when point-source noises are added, and the trained acoustic models not only perform well in the distant- talking scenario but also provide better results in the close-talking scenario.