Fake voice detection method based on dual-track differential modeling

Abstract

A fake voice detection method based on dual-track differential modeling includes: converting, by a pre-trained single/dual-track voice conversion model, a single-track audio into a stereo; extracting left and right-track Mel spectrogram features; performing texture enhancement on absolute values of differences between an original single-track Mel spectrogram feature and each of the left and right-track Mel spectrogram features; inputting texture enhancement results into left and right dual-branch feature extractors respectively to acquire a final attention map through feature extraction, processing and fusion; and inputting the final attention map into an attention pooling layer and a final binary classification layer to acquire a fake voice detection result. The fake voice detection method achieves fake voice detection by means of dual-track differential modeling, fine-grained texture enhancement, and multi-head attention feature fusion, improving the accuracy, transferability, and generalization of fake voice detection.

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