Source cell-phone recognition from recorded speech using non-speech segments
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Tarih
2014
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Academic Press Inc Elsevier Science
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yield higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42% to 98.39% and equal error rate (EER) reduces from 1.20% to 0.47% when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise. (C) 2014 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
Source cell-phone recognition, Mel-frequency cepstrum coefficients, Mutual information, Source microphone identification, Gaussian mixture model
Kaynak
Digital Signal Processing
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
35
