On the Use of Complementary Spectral Features for Speaker Recognition. (13th December 2007)

• Record Type: Journal Article
• Authors: Hosseinzadeh Hosseinzadeh, Danoush Danoush; Krishnan Krishnan, Sridhar Sridhar
• Other Names: Lee Lee Tan Tan Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2008(2008)
• Publication Date: 2007-12-13
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2008/258184
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : The most popular features for speaker recognition are Mel frequency cepstral coefficients (MFCCs) and linear prediction cepstral coefficients (LPCCs). These features are used extensively because they characterize the vocal tract configuration which is known to be highly speaker-dependent. In this work, several features are introduced that can characterize the vocal system in order to complement the traditional features and produce better speaker recognition models. The spectral centroid (SC), spectral bandwidth (SBW), spectral band energy (SBE), spectral crest factor (SCF), spectral flatness measure (SFM), Shannon entropy (SE), and Renyi entropy (RE) were utilized for this purpose. This work demonstrates that these features are robust in noisy conditions by simulating some common distortions that are found in the speakers' environment and a typical telephone channel. Babble noise, additive white Gaussian noise (AWGN), and a bandpass channel with 1 dB of ripple were used to simulate these noisy conditions. The results show significant improvements in classification performance for all noise conditions when these features were used to complement the MFCC andΔ MFCC features. In particular, the SC and SCF improved performance in almost all noise conditions within the examined SNR range (10–40 dB). For example, in cases where there was only one source of distortion, classification improvements of up to 8% and 10% were achieved under babble noise and AWGN, respectively, using the SCF feature.