  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, we analyze the suitability of state-of-the-art synthetic
speech detectors under additive noise with a special focus on front-end
features. Our comparison includes eight acoustic feature sets, five related to
spectral magnitude and three to spectral phase information. Our extensive
experiments on ASVSpoof 2015 corpus reveal several important findings. Firstly,
all the countermeasures break down even at relatively high signal-to-noise
ratios (SNRs) and fail to generalize to noisy conditions. Secondly, speech
enhancement 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 coefficients (MFCCs) and subband spectral
centroid magnitude coefficients (SCMCs) 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.
