Source cell-phone identification from recorded speech using non-speech segments - Anshan PEI; Rangding WANG; Diqun YAN (Dianxin kexue)
Source cell-phone identification has become a hot topic in multimedia forensics.A novel cell-phone identification method was proposed based on the silent segments of recorded speech.Firstly,the silent segments were obtained using adaptive endpoint detection algorithm.Then,the mean of Mel frequency coefficients (MFC) was extracted as the characteristics for device identification.Finally,the CfsSubsetEval evaluation function of WEKA platform was selected according to the best priority (BestFirst) search,and support vector machine (SVM) was used for classification.Twenty-three popular models of the cell-phones were evaluated in the experiment.Experimental results show that the proposed method is feasible and the average recognition rates are 99.23% and 99.00% on the TIMIT database and the CKC-SD database.At the same time,the proposed feature performs was demonstrated better than the MFC features and the Mel frequency cepstrum coefficients (MFCC) features of the speech segments.