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Voice imitation is mimicry of another speaker’s voice characteristics and speech behavior. Professional voice mimicry can create entertaining, yet realistic sounding target speaker renditions. As mimicry tends to exaggerate prosodic, idiosyncratic and lexical behavior, it is unclear how modern spectral-feature automatic speaker verification systems respond to mimicry “attacks”. We study the vulnerability of two well-known speaker recognition systems, traditional Gaussian mixture model – universal background model (GMM-UBM) and a state-of-the-art i-vector classifier with cosine scoring. The material consists of one professional Finnish imitator impersonating five wellknown Finnish public figures. In a carefully controlled setting, mimicry attack does slightly increase the false acceptance rate for the i-vector system, but generally this is not alarmingly large in comparison to voice conversion or playback attacks. Index Terms: Voice imitation, speaker recognition, mimicry attack

International audience; In this paper we study automatic regularization techniques for the fusion of automatic speaker recognition systems. Parameter regularization could dramatically reduce the fusion training time. In addition, there will not be any need for splitting the development set into different folds for cross-validation. We utilize majorization-minimization approach to automatic ridge regression learning and design a similar way to learn LASSO reg-ularization parameter automatically. By experiments we show improvement in using automatic regularization.

Human judgment is the final authority in forensic speaker recognition, but the use of modern speaker verification systems with accurate algorithms to perform the task under various circumstances has a huge potential to help the expert. The ultimate goal is to improve the accuracy of automatic systems when challenging data is provided and find a methodology for human-aided speaker recognition systems. This work presents an evaluation of speaker recognition carried out by human listeners and a gender dependent i-vector recognizer with a strategy for fusion of the decision process. Our experiments with HASR 2010 and HASR 2012 data indicate complementarity in

Text-independent speaker verification under additive noise corruption is considered. In the popular mel-frequency cepstral coefficient (MFCC) front-end, the conventional Fourier-based spectrum estimation is substituted with weighted linear predictive methods, which have earlier shown success in noise-robust speech recognition. Two temporally weighted variants of linear predictive modeling are introduced to speaker verification and they are compared to FFT, which is normally used in computing MFCCs, and to conventional linear prediction. The effect of speech enhancement (spectral subtraction) on the system performance with each of the four feature representations is also investigated. Experiments by the authors on the NIST 2002 SRE corpus indicate that the accuracy of the conventional and proposed features are close to each other on clean data. For factory noise at 0 dB SNR level, baseline FFT and the better of the proposed features give EERs of 17.4% and 15.6%, respectively. These accuracies improve to 11.6% and 11.2%, respectively, when spectral subtraction is included as a preprocessing method. The new features hold a promise for noise-robust speaker verification.

We propose the use of eye-movements as a biometric. A case study investigating potentials of eye-movement data for biometric purposes was conducted. Twelve participants' eye-movements were measured during still and moving objects viewing. The measured data includes pupil sizes and their dynamics, gaze velocities and distances of infrared reflections of the eyes. For still object viewing of 1 second duration, identification rate of 60 % can be obtained by using dynamics of pupil diameters. We suggest an integration of the eye-movement-based identification into general video-based biometric systems.

This paper describes the derivation of a sequence kernel that transforms speech utterances into probabilistic vectors for classification in an expanded feature space. The sequence kernel is built upon a set of Gaussian basis functions, where half of the basis functions contain speaker specific information while the other half implicates the common characteristics of the competing background speakers. The idea is similar to that in the Gaussian mixture model – universal background model (GMM-UBM) system, except that the Gaussian densities are treated individually in our proposed sequence kernel, as opposed to two mixtures of Gaussian densities in the GMM-UBM system. The motivation is to exploit the individual Gaussian components for better speaker discrimination. Experiments on NIST 2001 SRE corpus show convincing results for the probabilistic sequence kernel approach.

Support vector machine (SVM) equipped with sequence kernel has been proven to be a powerful technique for speaker verification. A number of sequence kernels have been recently proposed, each being motivated from different perspectives with diverse mathematical derivations. Analytical comparison of kernels becomes difficult. To facilitate such comparisons, we propose a generic structure showing how different levels of cues conveyed by speech utterances, ranging from low-level acoustic features to highlevel speaker cues, are being characterized within a sequence kernel. We then identify the similarities and differences between the popular generalized linear discriminant sequence (GLDS) and GMM supervector kernels, as well as our own probabilistic sequence kernel (PSK). Furthermore, we enhance the PSK in terms of accuracy and computational complexity. The enhanced PSK gives competitive accuracy with the other two kernels. Fusing all the three kernels yields an EER of 4.83% on the 2006 NIST SRE core test.

We adopt automatic language recognition methods to study dialect levelling — a phenomenon that leads to reduced structural differences among dialects in a given spoken language. In terms of dialect characterisation, levelling is a nuisance variable that adversely affects recognition accuracy: the more similar two dialects are, the harder it is to set them apart. We address levelling in Finnish regional dialects using a new SAPU (Satakunta in Speech) corpus containing material from Satakunta (South-Western Finland) between 2007 and 2013. To define a compact and universal set of sound units to characterize dialects, we adopt speech attributes features, namely manner and place of articulation. It will be shown that speech attribute distributions can indeed characterise differences among dialects. Experiments with an i-vector system suggest that (1) the attribute features achieve higher dialect recognition accuracy and (2) they are less sensitive against age-related levelling in comparison to traditional spectral approach. Index Terms: Finnish language, dialect levelling, social factors, native dialects, dialect detection, attributes detection, ivector modelling.

We study the problem of vocal effort mismatch in speaker verification. Changes in speaker’s vocal effort induce changes in fundamental frequency (F0) and formant structure which introduce unwanted intra-speaker variations to features. We compare seven alternative spectrum estimators in the context of melfrequency cepstral coefficient (MFCC) extraction for speaker verification. The compared variants include traditional FFT spectrum and six parametric all-pole models. Experimental results on the NIST 2010 speaker recognition evaluation (SRE) corpus utilizing both GMM-UBM and more recent GMM supervector classifier indicate that spectrum estimation has a considerable impact on speaker verification accuracy under mismatched vocal effort conditions. The highest recognition accuracy was achieved using a particular variant of temporally weighted all-pole model, stabilized weighted linear prediction (SWLP).

Popular features for speech processing, such as mel-frequency cepstral coefficients (MFCCs), are derived from the short-term magnitude spectrum, whereas the phase spectrum remains unused. While the common argument to use only the magnitude spectrum is that the human ear is phase-deaf, phase-based features have remained less explored due to additional signal processing difficulties they introduce. A useful representation of the phase is the group delay function, but its robust computation remains difficult. This paper advocates the use of group delay functions derived from parametric all-pole models instead of their direct computation from the discrete Fourier transform. Using a subset of the vocal effort data in the NIST 2010 speaker recognition evaluation (SRE) corpus, we show that group delay features derived via parametric all-pole models improve recognition accuracy, especially under high vocal effort. Additionally, the group delay features provide comparable or improved accuracy over conventional magnitude-based MFCC features. Thus, the use of group delay functions derived from all-pole models provide an effective way to utilize information from the phase spectrum of speech signals. Index Terms: speaker verification, group delay functions, high vocal effort