AI-Based Deepfake Voice Detection Using MFCC Features and Random Forest Classification

The rapid proliferation of AI-generated audio poses a serious threat to digital forensics, voice-based authentication, and information integrity. This paper presents a deepfake voice detection system that combines Mel-Frequency Cepstral Coefficient (MFCC) feature extraction with a Random Forest ensemble classifier to distinguish real human speech from synthetically generated audio. The proposed system processes input audio files in WAV or MP3 format, extracts 40 MFCC coefficients as the feature representation, and classifies each sample as real or fake through a trained Random Forest model. The complete pipeline is deployed as a Flask-based web application, enabling browser-based access without requiring any specialist software.

Experimental evaluation was conducted on a balanced binary dataset comprising 300 real voice recordings and 300 AI-generated voice samples (total: 600 samples), split 80/20 for training and testing. The system was evaluated against two baseline classifiers under identical feature conditions. Results demonstrate that the proposed Random Forest model achieves an accuracy of 92.7%, precision of 91.9%, recall of 93.5%, and an F1-score of 92.7%, indicating strong effectiveness for practical deepfake audio detection. These results represent a substantial improvement over the SVM baseline (accuracy: 76.4%) and Decision Tree baseline (accuracy: 81.2%).

Deepfake Audio Detection, MFCC Feature Extraction, Random Forest Classifier, Voice Cloning, Flask Web Application, Audio Forensics, Fake Speech Detection, Machine Learning, ASVspoof

1. T. Kinnunen, M. Sahidullah, H. Delgado, M. Todisco, N. Evans, J. Yamagishi, and K. A. Lee, "The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection," in Proc. Interspeech, Stockholm, Sweden, 2017, pp. 2–6. [Google Scholar] [Crossref]

2. A. Nautsch, X. Wang, N. Evans, T. Kinnunen, V. Vestman, M. Todisco, H. Delgado, M. Sahidullah, J. Yamagishi, and K. A. Lee, "ASVspoof 2019: Spoofing Countermeasures for the Detection of Synthesized, Converted and Replayed Speech," IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3, no. 2, pp. 252–265, 2021. [Google Scholar] [Crossref]

3. M. Sahidullah, T. Kinnunen, and C. Hanilci, "A Comparison of Features for Synthetic Speech Detection," in Proc. Interspeech, Dresden, Germany, 2015, pp. 2087–2091. [Google Scholar] [Crossref]

4. G. Lavrentyeva, S. Novoselov, A. Malinin, A. Kozlov, O. Kudashev, and V. Shchemelinin, "STC Antispoofing Systems for the ASVspoof 2019 Challenge," in Proc. Interspeech, Graz, Austria, 2019, pp. 1033–1037. [Google Scholar] [Crossref]

5. H. Zhang, M. Tan, and X. Zhang, "Fake Speech Detection Using Residual Network with Transformer Encoder," in Proc. ACM Workshop on Information Hiding and Multimedia Security, 2021, pp. 13–22. [Google Scholar] [Crossref]

6. R. Reimao and V. Tzerpos, "FoR: A Dataset for Synthetic Speech Detection," in Proc. International Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2019, pp. 1–8. [Google Scholar] [Crossref]

7. B. Li, L. Wang, T. Xu, and X. Li, "An Efficient Model for Real-Time Fake Voice Detection," Scientific Reports, vol. 13, no. 1, p. 7867, 2023. [Google Scholar] [Crossref]

8. M. Alzantot, B. Balaji, and M. Srivastava, "Did You Hear That? Adversarial Examples Against Automatic Speech Recognition," arXiv preprint arXiv:1801.00554, 2019. [Google Scholar] [Crossref]

9. M. Todisco, X. Wang, V. Vestman, M. Sahidullah, H. Delgado, A. Nautsch, J. Yamagishi, N. Evans, T. Kinnunen, and K. A. Lee, "ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection," in Proc. Interspeech, Graz, Austria, 2019, pp. 1008–1012. [Google Scholar] [Crossref]

10. H. Tak, J. Patino, M. Todisco, A. Nautsch, N. Evans, and J. Yamagishi, "End-to-End Anti-Spoofing with RawNet2," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada, 2021, pp. 6369–6373. [Google Scholar] [Crossref]

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