http://cs.uef.fi/pages/tkinnu/webpage/pdf/spoofingsurvey2014_SPCOM.pdf

Spoofing and countermeasures for speaker verification

While biometric authentication has advanced significantly in recent years, evidence shows the technology can be susceptible to malicious spoofing attacks. The research community has resp onded with dedicated countermeasures which aim to detect and deflect such attacks. Even if the literature shows that they can be effective, the problem is far from being solved; biometric systems remain vulnerable to spoofing. Despite a growing momentum to develo p spoofing countermeasures for automatic speaker verificati on, now that the technology has matured suffi ciently to support mass deployment in an array of diverse applications, greater effort will be needed in the future to ensure adequate protection against spoofing. This article provides a survey of past work and ide ntifies priority research directions for the future. We summarise previous studies involving impersonation, replay, speech synthesis and voice conversion spoofing attacks and more recent e fforts to develop dedicated countermeasures. The survey shows that future research should address the lack of standard datasets and the over-fitting of existing countermeasures to specific, know n spoofing attacks.

http://www.cs.joensuu.fi/pages/tkinnu/webpage/pdf/spoofingsurvey2014_SPCOM.pdf

Spoofing and countermeasures for speaker verification: a sur vey

This paper presents the main paradigms for speaker identification, and recent work on missing data methods to increase robustness. The feature extraction, speaker modeling and system classification are discussed. Evaluations of speaker identification performance subject to environmental noise are presented. While performance is impressive in clean speech conditions, there is rapid degradation with mismatched additive noise. Missing data methods can compensate against arbitrary disturbances and remove environmental mismatches. An overview of missing data methods is provided and applications to robust speaker identification summarized. Finally combined approaches involving bottom-up estimation and top-down processing are reviewed, and their significance discussed.

An Overview of Speaker Identification: Accuracy and Robustness Issues

In conventional speaker recognition method based on MFCC, the phase information has been ignored. In this paper, we proposed a method that integrates the phase information on a speaker recognition method. The speaker identification experiments were performed using NTT database which consists of sentences uttered at normal speed mode by 35 Japanese speakers (22 males and 13 females) on five sessions over ten months. Each speaker uttered only 5 training utterances (about 20 seconds in total). Using the phaseinformation, the speaker recognition error rate was reduced by about 44%. Index Terms: speaker identification, MFCC, phase information, GMM, combination method

Speaker Recognition by Combining MFCC and Phase Information

Conventional speaker recognition systems perform poorly under noisy conditions. Inspired by auditory perception, computational auditory scene analysis (CASA) typically segregates speech by producing a binary time-frequency mask. We investigate CASA for robust speaker identification. We first introduce a novel speaker feature, gammatone frequency cepstral coefficient (GFCC), based on an auditory periphery model, and show that this feature captures speaker characteristics and performs substantially better than conventional speaker features under noisy conditions. To deal with noisy speech, we apply CASA separation and then either reconstruct or marginalize corrupted components indicated by a CASA mask. We find that both reconstruction and marginalization are effective. We further combine the two methods into a single system based on their complementary advantages, and this system achieves significant performance improvements over related systems under a wide range of signal-to-noise ratios.

/pdf/casa-based-robust-speaker-identification-2m24zduajs.pdf

CASA-Based Robust Speaker Identification

Missing data techniques have been recently applied to speaker recognition to increase performance in noisy environments. The drawback of these techniques is the vulnerability of the recognizer to errors in the classification of time-frequency points as corrupt or reliable. In this paper we propose the combination of missing data processing and feature selection to reduce these errors. The formation of a set of speaker discriminative features allows time-frequency reliability masks to be refined via the removal of the non-discriminative frequency sub-bands. The reduced set is selected dynamically using multi-condition training and an estimate of the global SNR allowing for efficient top-down processing. Experimental results show that the combined technique achieves significant improvement over traditional bottom-up processing thus demonstrating the validity of the approach.

Robust speaker identification using combined feature selection and missing data recognition

This work investigates the use of missing data techniques for noise robust speaker identification. Most previous work in this field relies on the diagonal covariance assumption in modeling speaker specific characteristics via Gaussian mixture models. This paper proposes the use of full covariance models that can capture linear correlations among feature components. This is of importance for missing data marginalization techniques as they depend on spectral rather than cepstral feature representations. Bounded and complete marginalization schemes are investigated both with diagonal and full covariance mixture models. Speaker identification experiments using stationary and non-stationary noise confirm that full covariance models are indeed superior compared to diagonal models.

Towards the use of full covariance models for missing data speaker recognition

Sub-band Partitioning for Full Covariance based Missing Data Speaker Recognition

Despite their effectiveness for robust speech processing, missing data techniques are vulnerable to errors in the classification of the input speech signal's time-frequency points. A direct method for the removal of these mask errors is through the top-down optimization of the estimated mask, however this requires a measure to evaluate the mask quality without a priori noise knowledge. In this paper we propose the normalized likelihood confidence as such a criterion for robust speaker recognition. In this approach the accuracy with which an estimated mask classifies time-frequency points as corrupt or reliable is related to its likelihood score confidence. This is based on the conceptual effect of binary mask errors on the model likelihood distributions produced by accumulated marginalization densities. Experimental results confirm a relationship between the normalized likelihood distance and the accuracy of the time-frequency mask produced by various estimation strategies.

Daniel Pullella

Papers

An Overview of Speaker Identification: Accuracy and Robustness Issues

Robust speaker identification using combined feature selection and missing data recognition

Towards the use of full covariance models for missing data speaker recognition

Sub-band Partitioning for Full Covariance based Missing Data Speaker Recognition

A criterion for the enhancement of time-frequency masks in missing data recognition