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This paper describes text-dependent speaker verification as a task involving four classes of trials depending on whether the target speaker or an impostor pronounces the expected pass-phrase or not. These four classes are used to reformulate the log-likelihood ratio traditionally used in text-independent speaker verification. Three formulations of the alternative hypothesis are considered, leading...
This work focuses on text-dependent speaker verification, where a user is required to chose and pronounce a customized pass-phrase to get authenticated. In this context, there are three types of impostures: an impostor pronouncing the correct pass-phrase, an impostor pronouncing a wrong pass-phrase and the most difficult one: an impostor playing back a recording of the target speaker pronouncing a...
The potential for biometric systems to be manipulated through some form of subversion is well acknowledged. One such approach known as spoofing relates to the provocation of false accepts in authentication applications. Another approach referred to as obfuscation relates to the provocation of missed detections in surveillance applications. While the automatic speaker verification research community...
In this paper, we study the use of two kinds of kernel-based discriminative models, namely support vector machine (SVM) and deep neural network (DNN), for speaker verification. We treat the verification task as a binary classification problem, in which a pair of two utterances, each represented by an i-vector, is assumed to belong to either the “within-speaker” group or the “between-speaker” group...
This paper analyses the probabilistic linear discriminant analysis (PLDA) speaker verification approach with limited development data. This paper investigates the use of the median as the central tendency of a speaker's i-vector representation, and the effectiveness of weighted discriminative techniques on the performance of state-of-the-art length-normalised Gaussian PLDA (GPLDA) speaker verification...
Many studies have proven the effectiveness of discriminative training for speaker verification based on probabilistic linear discriminative analysis (PLDA) with i-vectors as features. Most of them directly optimize the log-likelihood ratio score function of the PLDA model instead of explicitly train the PLDA model. But this optimization process removes some of the constraints that normally are imposed...
In this paper we report on speaker verification experiments using branched vocal tract model estimates of alveolar nasal (/n/) stops. While the discriminatory potential of nasal acoustics has long been established, their acoustic properties have so far mostly been characterized using spectral features. Here, we used a Bayesian estimation technique to obtain reflection coefficients of a branched-tube...
Co-channel speech, which occurs in monaural audio recordings of two or more overlapping talkers, poses a great challenge for automatic speech applications. Automatic speech recognition (ASR) performance, in particular, has been shown to degrade significantly in the presence of a competing talker. In this paper, assuming a known target talker scenario, we present two different masking strategies based...
Communication system mismatch represents a major influence for loss in speaker recognition performance. While microphone and handset differences have been considered in the NIST SRE, nonlinear communication system differences, such as modulation/demodulation (Mod/DeMod) carrier drift, have yet to be considered. In this study, an algorithm for estimating and correcting Mod/DeMod frequency offsets distortion...
In this paper we investigate the use of deep neural networks (DNNs) for a small footprint text-dependent speaker verification task. At development stage, a DNN is trained to classify speakers at the framelevel. During speaker enrollment, the trained DNN is used to extract speaker specific features from the last hidden layer. The average of these speaker features, or d-vector, is taken as the speaker...
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