Automatic Telephone Handset Identification by Sparse Representation of Random Spectral Features

摘要

Speech signals convey information not only for speakers' identity and the spoken language, but also for the acquisition devices used during their recording. Therefore, it is reasonable to perform acquisition device identification by analyzing the recorded speech signal. To this end, the random spectral features (RSFs) are proposed as an intrinsic fingerprint suitable for device identification. The RSFs are extracted from each speech signal by first averaging its spectrogram along the time axis and then by projecting the re-suiting mean spectrogram onto a Gaussian random matrix of compatible dimensions. By applying a sparse-representation based classifier to the device RSFs, state-of-the-art identification accuracy of 95.55% has been obtained on a set of 8 telephone handsets, from Lincoln-Labs Handset Database (LLHDB).