Abstract
The advancement of deep learning technology in speech generation has made fake speech almost perceptually indistinguishable from real speech. Most of the attempts in literature are dataset dependent and fail to detect fake speech in domain variability or cross-dataset scenarios. This study explores the potential of excitation source features to detect fake speech in domain variable conditions. Motivated by the distinction observed using excitation source information, this work proposes a new feature called residual modulation spectrogram for fake speech detection. ResNet-34 is used for the binary classification task to distinguish between fake and real speech. The modulation spectrogram is used as the baseline feature. The proposed approach performs well in domain variability in most cases and shows generalizability across different datasets. Additionally, the score-level combination of residual modulation spectrogram and modulation spectrogram shows enhanced performance. This justifies the efficacy of source and system information in domain variability scenarios.
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Acknowledgement
The authors would like to acknowledge the Ministry of Electronics and Information Technology (MeitY), Government of India for funding this research work and “Anantganak”, the high performance computation (HPC) facility, IIT Dharwad, for enabling us to perform our experiments.
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Sadashiv T. N., R., Kumar, D., Agarwal, A., Tzudir, M., Mishra, J., Prasanna, S.R.M. (2023). Source and System-Based Modulation Approach for Fake Speech Detection. In: Karpov, A., Samudravijaya, K., Deepak, K.T., Hegde, R.M., Agrawal, S.S., Prasanna, S.R.M. (eds) Speech and Computer. SPECOM 2023. Lecture Notes in Computer Science(), vol 14338. Springer, Cham. https://doi.org/10.1007/978-3-031-48309-7_12
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