Cell phone verification from speech recordings using sparse representation
Source recording device recognition is an important emerging research field of digital media forensic. Most of the prior literature focus on the recording device identification problem. In this study we propose a source cell phone verification scheme based on sparse representation. We employed Gaussian supervectors (GSVs) based on Mel-frequency cepstral coefficients (MFCCs) extracted from the speech recordings to characterize the intrinsic fingerprint of the cell phone.
For the sparse representation, both exemplar based dictionary and dictionary learned by K-SVD algorithm were examined to this problem. Evaluation experiments were conducted on a corpus consists of speech recording recorded by 14 cell phones. The achieved equal error rate (EER) demonstrated the feasibility of the proposed scheme.