Joint Factor Analysis Versus Eigenchannels in Speaker Recognition

Kenny, Patrick; Boulianne, Gilles; Ouellet, Pierre; Dumouchel, Pierre

doi:10.1109/tasl.2006.881693

Cited by 628 publications

(348 citation statements)

References 15 publications

(43 reference statements)

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“…The preliminary experiments of [3,8] were reported on the NIST 2002 and 2006 SRE corpora using a lightweight Gaussian mixture model-universal background model (GMM-UBM) system [17] and generalized linear discriminant sequence support vector machine (GLDS-SVM) without any session variability compensation techniques. The recent results of [36], using multi-taper MFCC features only, were reported on NIST 2002 and 2008 SRE corpora using GMM-UBM, GMM-SVM and joint factor analysis (JFA) [38,39] classifiers.…”

Section: Introductionmentioning

confidence: 99%

Multitaper MFCC and PLP features for speaker verification using i-vectors

Alam

Kinnunen

Kenny

et al. 2013

Speech Communication

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In this paper we study the performance of the low-variance multi-taper Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features in a state-ofthe-art i-vector speaker verification system. The MFCC and PLP features are usually computed from a Hamming-windowed periodogram spectrum estimate. Such a singletapered spectrum estimate has large variance, which can be reduced by averaging spectral estimates obtained using a set of different tapers, leading to a so-called multitaper spectral estimate. The multi-taper spectrum estimation method has proven to be powerful especially when the spectrum of interest has a large dynamic range or varies rapidly. Multi-taper MFCC features were also recently studied in speaker verification with promising preliminary results. In this study our primary goal is to validate those findings using an up-to-date i-vector classifier on the latest NIST 2010 SRE data. In addition, we also propose to compute robust perceptual linear prediction (PLP) features using multitapers. Furthermore, we provide a detailed comparison between different taper weight selections in the Thomson multi-taper method in the context of speaker verification. Speaker verification results on the telephone (det5) and microphone speech (det1, det2, det3 and det4) of the latest NIST 2010 SRE corpus indicate that the multitaper methods outperform the conventional periodogram technique. Instead of simply averaging (using uniform weights) the individual spectral estimates in forming the multitaper estimate, weighted averaging (using non-uniform weights) improves performance.Compared to the MFCC and PLP baseline systems, the sine-weighted cepstrum estimator 2 (SWCE) based multitaper method provides average relative reductions of 12.3% and 7.5% in equal error rate, respectively. For the multi-peak multi-taper method, the corresponding reductions are 12.6% and 11.6%, respectively. Finally, the Thomson multitaper method provides error reductions of 9.5% and 5.0% in EER for MFCC and PLP features, respectively. We conclude that both the MFCC and PLP features computed via multitapers provide systematic improvements in recognition accuracy.

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Section: Introductionmentioning

confidence: 99%

Multitaper MFCC and PLP features for speaker verification using i-vectors

Alam

Kinnunen

Kenny

et al. 2013

Speech Communication

Self Cite

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“…Many techniques have been proposed with the most notable systems based on Gaussian mixture model (GMM), inter-session variability (ISV) modeling [10], joint factor analysis (JFA) [16], and i-vectors [11].…”

Section: Vulnerability Of Voice Biometricsmentioning

confidence: 99%

Presentation attack detection in voice biometrics

Korshunov¹,

Marcel²

2017

User-Centric Privacy and Security in Biometrics

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Recent years have shown an increase in both the accuracy of biometric systems and their practical use. The application of biometrics is becoming widespread with fingerprint sensors in smartphones, automatic face recognition in social networks and video-based applications, and speaker recognition in phone banking and other phone-based services. The popularization of the biometric systems, however, exposed their major flaw -high vulnerability to spoofing attacks [1]. A fingerprint sensor can be easily tricked with a simple glue-made mold, a face recognition system can be accessed using a printed photo, and a speaker recognition system can be spoofed with a replay of pre-recorded voice. The ease with which a biometric system can be spoofed demonstrates the importance of developing efficient anti-spoofing systems that can detect both known (conceivable now) and unknown (possible in the future) spoofing attacks.Therefore, it is important to develop mechanisms that can detect such attacks, and it is equally important for these mechanisms to be seamlessly integrated into existing biometric systems for practical and attack-resistant solutions. To be practical, however, an attack detection should have (i) high accuracy, (ii) be well-generalized for different attacks, and (iii) be simple and efficient.One reason for the increasing demand for effective presentation attack detection (PAD) systems is the ease of access to people's biometric data. So often, a potential attacker can almost effortlessly obtain necessary biometric samples from social networks, including facial images, audio and video recordings, and even extract fingerprints from high resolution images. Therefore, various privacy protection solutions, such as legal privacy requirements and algorithms for obfuscating personal information, e.g., visual privacy filters [2], as well as, social awareness of threats to privacy can also increase security of personal information and potentially reduce the vulnerability of biometric systems.In this chapter, however, we focus on presentation attacks detection in voice biometrics, i.e., automatic speaker verification (ASV) systems. We discuss vulnerabilities of these systems to presentation attacks (PAs), present different state of the art 1

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“…Speaker recognition technology can be transferred into a sound recognition problem, so the sound recognition can be built based on GMM [12][13][14][15][16][17][18][19]. For a given sound feature vector {X t }, t=1,2,..., T, assuming we have K classes of aircraft engine sound which is different from each other, the purpose of sound recognition is to find the class of sound k, whose corresponding GMM model k obtain the largest posterior probability P( k/X).…”

Section: Universal Background Modelmentioning

confidence: 99%

Recognition of Aircraft Engine Sound Based on GMM-UBM Model

Yuan¹,

Sun

Yang

2017

MATEC Web Conf.

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Abstract-Gaussian mixture model-universal background model (GMM-UBM) is a commonly-used speaker recognition technology, and which has achieved good effect for detection speaker's sound. In this paper, we explore GMM-UBM method for use with abnormal aircraft engine sound detection. We designed a GMM-UBM based aircraft engine sound recognition system, which extracts MFCC feature parameters and trains the GMM-UBM models using maximum a posteriori (MAP) adaptive algorithm. Experimental results show the GMM-UBM based aircraft engine sound recognition system can achieve higher recognize rate in real-word aircraft engine sound test.

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Joint Factor Analysis Versus Eigenchannels in Speaker Recognition

Cited by 628 publications

References 15 publications

Multitaper MFCC and PLP features for speaker verification using i-vectors

Multitaper MFCC and PLP features for speaker verification using i-vectors

Presentation attack detection in voice biometrics

Recognition of Aircraft Engine Sound Based on GMM-UBM Model

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