A comparison of features for synthetic speech detection

By Md. Sahidullah, Tomi H. Kinnunen, Cemal Hanilçi. Interspeech, 2015

434 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/128a71d926c7eb5369f7d2aed87207dcc8079c39

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Citations in the corpus

A New Feature for Automatic Speaker Verification Anti-Spoofing: Constant Q Cepstral Coefficients (2016, background): “Results are illustrated independently for known and unknown attacks and the average.”
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech (2019, methodology): “Both use a common Gaussian mixture model (GMM) back-end classifier with either constant-Q cepstral coefficient (CQCC) features [64, 65] (B01) or linear frequency cepstral coefficient (LFCC) features [66] (B02).”
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection (2019, methodology): “ms. They use a common Gaussian mixture model (GMM) back-end classiﬁer with either constant Q cepstral coefﬁcient (CQCC) features [22, 23] (B01) or linear frequency cepstral coefﬁcient (LFCC) features [24] (B02). 4. Challenge results Table 1 shows results4 in terms of the t-DCF and EER for primary sy”
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan (2021, neutral): “They include: a high resolution LFCCGMM [4], [5] system; a CQCC-GMM [6], [7] system; an LFCC-LCNN [8] system; a RawNet2 [9] system.”
STC Antispoofing Systems for the ASVspoof2019 Challenge (2019, methodology): “Additionally, we considered cepstral coefficients from baseline systems, proposed by the organisers of the ASVspoof2019: Linear Frequency Cepstral Coefficients (LFCC) [7] obtained by the use of triangular filters in linear space for local integration of the power spectrum and Constant Q Cepstral Coe”
Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks (2017, neutral): “[Identity:] φ(y) = y [Static & delta [52]:] φ(y) = Wy”
End-to-End anti-spoofing with RawNet2 (2020, background): “These choices are motivated by the success of linear and inverseMel [21] front-ends for anti-spoofing.”
ASVspoof 2019: Automatic Speaker Veriﬁcation Spooﬁng and Countermeasures Challenge Evaluation Plan ∗ (2021, background): “They are based on two diﬀerent acoustic frontends, namely linear frequency cepstral coeﬃcients [16] (LFCC) and constant Q cepstral coeﬃcients [17] (CQCC).”