Blind detection of electronic disguised voice
Since voice disguise has great negative impact on establishing authenticity of audio evidence in forensics, and has shown an increasing tendency in illegal applications, it is important to identify whether a suspected voice has been disguised or not. However, research on such detection has not been reported. In this paper, we focus on blind detection of electronic disguised voice. Statistical moments of Mel-frequency cepstrum coefficients (MFCC) are extracted as acoustic features of speech signals. Then an approach for detection of disguised voice based on the extracted features and Support Vector Machine (SVM) classifiers is proposed. The extensive experiments demonstrate that detection rates higher than 95% can be achieved, indicating that detection performance of the proposed approach is good.