H-Voice: Fake voice histograms (Imitation+DeepVoice)
Description
This data set consists of histograms of original voice recordings and fake voice recordings obtained by imitation [1, 2] and DeepVoice [3]. The histograms provided in this dataset can be used to train a machine learning system to classify original and fake voice recordings obtained with the imitation and DeepVoice algorithms. Each directory has the following composition: - Training_fake: 1080 histograms of fake voice recordings (1008 with imitation and with 72 DeepVoice) - Training_original: 1012 histograms of original voice recordings (1008 with imitation and with 4 DeepVoice) - Validation_fake: 432 histograms of fake voice recordings (all with imitation) - Validation_original: 432 histograms of original voice recordings (all with imitation) - External_test2_DeepVoice: 76 histograms with DeepVoice (4 original + 72 fake). The original histograms are named as ground_truth, the others are fake - External_test1: 760 histograms with imitation (380 original + 380 fake) References: [1] DM Ballesteros L, JM Moreno A. Highly transparent steganography model of speech signals using Efficient Wavelet Masking. Expert Systems with Applications 39 (10), 2012, 9141-9149, https://doi.org/10.1016/j.eswa.2012.02.066 [2] DM Ballesteros L, JM Moreno A. On the ability of adaptation of speech signals and data hiding, Expert Systems with Applications 39 (16), 2012, 12574-12579, https://doi.org/10.1016/j.eswa.2012.05.027 [3] S.O. Arik, M. Chrzanowski, A. Coates, G. Diamos, A. Gibiansky, Y. Kang, X. Li, J. Miller, A. Ng, J. Raiman, S. Sengupta, M. Shoeybi. Deep Voice: Real-time Neural Text-to-Speech. 2017. https://arxiv.org/abs/1702.07825
Files
Steps to reproduce
The histograms provided in this dataset can be used to train an machine learning system to classify original and fake voice recordings obtained with the imitation and DeepVoice algorithms.