Playback speech detection based on magnitude–phase spectrum

Yang, Jian; Liu, Leian

doi:10.1049/el.2018.0739

Cited by 24 publications

(27 citation statements)

References 10 publications

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“…In this study, similar to our previous playback speech detection studies [38,40,41], deep neural network (DNN) is selected as a suitable classifier because we found that DNN based systems can give better performance. The reason may be that DNN has both a classifier function and feature-learning ability [54].…”

Section: Evaluation Rule and Experimental Setupmentioning

confidence: 96%

“…Table 8 gives the comparison with some known systems based on hand-crafted features on ASVspoof 2017 V2 evaluation set. In which, logE represents logarithm energy, qDFTspe represents Q-log domain DFT-based mean normalized log spectral [42], eCQCC represents extended CQCC [38], CMPOC represents constant-Q magnitudephase octave coefficients [40] and CQSPIC represents constant-Q statistics-plus-principal information coefficients [41]. From Table 8, it can be seen that the performance of our systems are better than some other known systems.…”

Section: Comparison With Some Commonly Used Featuresmentioning

confidence: 96%

“…• CQT based features: which include constant-Q cepstral coefficients (CQCC) [34,35] used in [4,36,37], extended CQCC [38,39], constant-Q magnitudephase octave coefficients [40], and constant-Q statistics-plus-principal information coefficients [41].…”

Section: Related Workmentioning

confidence: 99%

“…In our previous work, octave segmentation was proposed to segment magnitude-phase spectrum [40] and octave power spectrum [53]. In this work, octave segmentation is used here to segment VMLMS (MMLMS) into un-overlapped blocks according to octave.…”

Section: Octave Segmentation and Discrete Cosine Transformmentioning

confidence: 99%

“…Therefore, the static dimension of CMOC is set as 108. Since our previous works [38,40] have shown static features will degrade the performance in playback speech detection, only dynamic features are used in this study. For different feature combinations of CMOC and CVOC dynamic features, D and A represent delta and acceleration, respectively.…”

Section: Evaluation Rule and Experimental Setupmentioning

confidence: 99%

See 4 more Smart Citations

Discriminative features based on modified log magnitude spectrum for playback speech detection

Yang

Ren³

et al. 2020

J AUDIO SPEECH MUSIC PROC.

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In order to improve the performance of hand-crafted features to detect playback speech, two discriminative features, constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients, are proposed for playback speech detection in this work. They rely on our findings that variance-based modified log magnitude spectrum and mean-based modified log magnitude spectrum can enhance the discriminative power between genuine speech and playback speech. Then constant-Q variance-based octave coefficients (constant-Q mean-based octave coefficients) can be obtained by combining variance-based modified log magnitude spectrum (mean-based modified log magnitude spectrum), octave segmentation, and discrete cosine transform. Finally, constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients are evaluated on ASVspoof 2017 corpus version 2.0 and ASVspoof 2019 physical access, respectively. Experimental results show that variance-based modified log magnitude spectrum and mean-based modified log magnitude spectrum can produce discriminative features toward playback speech. Further results on the two databases show that constant-Q variance-based octave coefficients and constant-Q mean-based octave coefficients can perform better than some common features, such as mel frequency cepstral coefficients and constant-Q cepstral coefficients.

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