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Hybrid CNN-LSTM Architectures for Deepfake Audio Detection Using Mel Frequency Cepstral Coefficients and Spectogram Analysis

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The hybrid model achieved a classification accuracy of 94.7%, surpassing standalone CNN and LSTM models.
It demonstrated exceptional generalization capabilities with a high AUC-ROC score of 97.3%, indicating reliable performance.
Further evaluations on the ASVspoof 2019 dataset confirmed robustness, achieving an accuracy of 93.2%.
The fusion of spectral and temporal features significantly enhances deepfake audio detection for cybersecurity applications.

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Hybrid deep CNN-LSTM architecture improves classification accuracy and generalization in deepfake audio detection.

Asuai et al. (2025) studied this question.

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