NDLI: Automatic telephone handset identification by sparse representation of random spectral features

Content Provider	ACM Digital Library
Author	Panagakis, Yannis Kotropoulos, Constantine
Abstract	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 resulting 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).
Starting Page	91
Ending Page	96
Page Count	6
File Format	PDF
ISBN	9781450314176
DOI	10.1145/2361407.2361422
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-09-06
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Digital speech forensics Random features Sparse representation
Content Type	Text
Resource Type	Article

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Automatic telephone handset identification by sparse representation of random spectral features