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Comparison of the effectiveness of cepstral coefficients for Russian speech synthesis detection

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Journal of Computer Virology and Hacking Techniques Aims and scope Submit manuscript

Abstract

Modern speech synthesis technologies can be used to deceive voice authentication systems, phone scams, or discredit public figures. An urgent task is to detect synthesized speech to protect against the threat of voice substitution attacks. The solution to this problem is based on the choice of cepstral coefficients that determine the quality of the cloned voice. In addition, the dataset used to train the neural network must match the language for which the synthesized speech will be detected. The paper discusses the most widely used cepstral coefficients and compares their effectiveness using two main types of neural networks. To train the network, the Russian speech dataset was developed, the use of which allows achieving the highest accuracy in determining speech synthesis in the case of deepfakes in Russian.

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Notes

  1. https://www.youtube.com/watch?v=yIUTvPOXkk8.

  2. https://arstechnica.com/information-technology/2023/01/microsofts-new-ai-can-simulate-anyones-voice-with-3-seconds-of-audio/.

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Acknowledgements

The work was funded by the Foundation for Assistance to Small Innovative Enterprises, Russia under Grant No (36ГУКoдИИC12-D7/81484).

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  1. National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Moscow, Russian Federation

    Dmitry Efanov, Pavel Aleksandrov & Ilia Mironov

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  1. Dmitry Efanov
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  2. Pavel Aleksandrov
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Correspondence to Dmitry Efanov.

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Efanov, D., Aleksandrov, P. & Mironov, I. Comparison of the effectiveness of cepstral coefficients for Russian speech synthesis detection. J Comput Virol Hack Tech (2023). https://doi.org/10.1007/s11416-023-00491-0

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