Abstract
Since voice disguise has been an increasing tendency in illegal applications, and it has a great negative impact on establishing the authenticity of audio evidence for audio forensics, it is important to be able to identify whether a suspected voice has been disguised or not. However, few studies on such identification have been reported. In this paper, we propose an algorithm to identify electronic disguised voices. Since voice disguise, in essence, the modification of the frequency spectrum of speech signals, and mel-frequency cepstrum coefficients (MFCCs) can be used to well describe frequency spectral properties, MFCC-based features are supposed to be effective for the identification of disguised voices. In this paper, MFCC statistical moments including mean values and correlation coefficients are extracted as acoustic features. Then, an algorithm based on the extracted features and support vector machine classifiers is proposed to separate disguised voices from original voices. Extensive experiments show that the detection rates higher than 90% of the voices from various speech databases and disguised by various methods can be achieved, indicating that the identification performance of this algorithm is remarkable.
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