Dataset for Speech Tampering Detection and Classification

Note that each sample contains a set of MSFs and the corresponding GTM.For DET-net training on Chinese speech data, we utilized 5,000 authentic samples and 20,000 tampered samples (5,000 per manipulation type), with 800 authentic and 3,200 tampered samples (800 per type) reserved for evaluation. For English speech data, the training set consisted of 4,500 authentic and 18,000 tampered samples (4,500 per type), while the evaluation set contained 800 authentic and 3,200 tampered samples (800 per type).

Note that for testing purposes, we constructed a balanced testing set comprising 800 authentic speech samples and 800 manipulated audio samples of a specific tampering type, which were then processed through the network for evaluation.For CLF-net training, we employed identical sample distributions for both Chinese and English speech data: 3,000 authentic and 12,000 tampered samples (3,000 per manipulation type) for training, with 1,000 authentic and 4,000 tampered samples (1,000 per type) allocated for evaluation.