Spoofing detection goes noisy: An analysis of synthetic speech detection in the presence of additive noise

Abstract

Automatic speaker verification (ASV) technology is recently finding its way to end-user applications for secure access to personal data, smart services or physical facilities. Similar to other biometric technologies, speaker verification is vulnerable to spoofing attacks where an attacker masquerades as a particular target speaker via impersonation, replay, text-to-speech (TTS) or voice conversion (VC) techniques to gain illegitimate access to the system. We focus on TTS and VC that represent the most flexible, high-end spoofing attacks. Most of the prior studies on synthesized or converted speech detection report their findings using high-quality clean recordings. Meanwhile, the performance of spoofing detectors in the presence of additive noise, an important consideration in practical ASV implementations, remains largely unknown. To this end, our study provides a comparative analysis of existing state-of-the-art, off-the-shelf synthetic speech detectors under additive noise contamination with a special focus on front-end processing that has been found critical. Our comparison includes eight acoustic feature sets, five related to spectral magnitude and three to spectral phase information. All the methods contain a number of internal control parameters. Except for feature post-processing steps (deltas and cepstral mean normalization) that we optimized for each method, we fix the internal control parameters to their default values based on literature, and compare all the variants using the exact same dimensionality and back-end system. In addition to the eight feature sets, we consider two alternative classifier back-ends: Gaussian mixture model (GMM) and i-vector, the latter with both cosine scoring and probabilistic linear discriminant analysis (PLDA) scoring. Our extensive analysis on the recent ASVspoof 2015 challenge provides new insights to the robustness of the spoofing detectors. Firstly, unlike in most other speech processing tasks, all the compared spoofing detectors break down even at relatively high signal-to-noise ratios (SNRs) and fail to generalize to noisy conditions even if performing excellently on clean data. This indicates both difficulty of the task, as well as potential to over-fit the methods easily. Secondly, speech enhancement pre-processing is not found helpful. Thirdly, GMM back-end generally outperforms the more involved i-vector back-end. Fourthly, concerning the compared features, the Mel-frequency cepstral coefficient (MFCC) and subband spectral centroid magnitude coefficient (SCMC) features perform the best on average though the winner method depends on SNR and noise type. Finally, a study with two score fusion strategies shows that combining different feature based systems improves recognition accuracy for known and unknown attacks in both clean and noisy conditions. In particular, simple score averaging fusion, as opposed to weighted fusion with logistic loss weight optimization, was found to work better, on average. For clean speech, it provides 88% and 28% relative improvements ...

ASVspoof: The Automatic Speaker Verification Spoofing and Countermeasures Challenge

“…Physical access scenarios may also be addressed in the future through the re-recording of spoofing attacks with a fixed microphone. e) Additive noise and reverberation: recent studies [100], [82], [83] indicate that some countermeasures offer little resistance to additive noise, with spoofing-detection performance degrading much more rapidly as a function of falling signal-tonoise ratio than is typical for ASV performance. This suggests either an intrinsic difficulty in the problem or what is possibly the result of countermeasures over-trained to the ASVspoof database, which consists of technically high-quality speech.…”

Deep Siamese Architecture Based Replay Detection for Secure Voice Biometric

“…Current countermeasures against replay attacks generally operate in one of three ways: (a) identifying exact reproduction of a previous access attempt; (b) exploiting differences in the speech transmission channel [9]; or (c) targeting artefacts in replayed speech such as pop-noise [10], and source features [11]. Commonly used spectral features include sub-band spectral centroid magnitude coefficients (SCMCs) [12], constant-Q cepstral coefficients (CQCCs) [13], single frequency filtering cepstral coefficients (SFF-CCs) [14], inverse Mel frequency cepstral coefficients (IMFCCs) [15], rectangular filter cepstral coefficients (RFCCs) [15], and scattering decomposition based features [16]. In addition, deep neural network (DNN) architectures have also been employed either as discriminative feature extractors [17] or as an end-toend spoofing detectors [18] in a number of ways.…”

A Deep Identity Representation for Noise Robust Spoofing Detection

“…The literature about ASV anti-spoofing in noisy conditions is scarce due to the novelty of this area. One of the first studies was carried out in [11], where the robustness of various frontend features were evaluated under different noisy conditions. In [10], a neural network was trained as an anti-spoofing detection system, and several front-end features were tested under five additive noises and reverberant conditions.…”