Telephone handset identification by feature selection and sparse representations

Abstract

Speech signals convey information not only for the 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) and the labeled spectral features (LSFs) are proposed as intrinsic fingerprints suitable for device identification. The RSFs and the LSFs are extracted by applying unsupervised and supervised feature selection to the mean spectrogram of each speech signal, respectively. State-of-the-art identification accuracy of 97.58% has been obtained by employing LSFs on a set of 8 telephone handsets, from Lincoln-Labs Handset Database (LLHDB).