Generalized Linear Kernels for One-Versus-All Classification: Application to Speaker Recognition

Hatch, Andrew; Stolcke, Andreas

doi:10.1109/icassp.2006.1661343

Cited by 44 publications

(34 citation statements)

References 4 publications

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“…The Within-Class Covariance Normalization (WCCN) technique is a normalization method, originally introduced in the context of Support Vector Machine (SVM) modelling [27]. The WCCN tries to minimize the expected classification error on the training data.…”

Section: Within-class Covariance Normalization (Wccn)mentioning

confidence: 99%

Face Recognition Using Simplified Probabilistic Linear Discriminant Analysis

Vesnicer

Gros

Pavešić

2012

International Journal of Advanced Robotic Systems

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Face recognition in uncontrolled environmentsremains an open problem that has not been satisfactorily solved by existing recognition techniques. In this paper, we tackle this problem using a variant of the recently proposed Probabilistic Linear Discriminant Analysis (PLDA). We show that simplified versions of the PLDA model, which are regularly used in the field of speaker recognition, rely on certain assumptions that not only result in a simpler PLDA model, but also reduce the computational load of the technique and -as indicated by our experimental assessments -improve recognition performance. Moreover, we show that, contrary to the general belief that PLDA-based methods produce well calibrated verification scores, score normalization techniques can still deliver significant performance gains, but only if nonparametric score normalization techniques are employed. Last but not least, we demonstrate the competitiveness of the simplified PLDA model for face recognition by comparing our results with the state-of-the-art results from the literature obtained on the second version of the large-scale Face Recognition Grand Challenge (FRGC) database.

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Section: Within-class Covariance Normalization (Wccn)mentioning

confidence: 99%

Face Recognition Using Simplified Probabilistic Linear Discriminant Analysis

Vesnicer

Gros

Pavešić

2012

International Journal of Advanced Robotic Systems

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“…Previously, the input i-vectors were conditioned by within-class covariance normalization (WCCN, [20]) and length normalized. In Table 4 we can see the best results for the individual subsystems.…”

Section: Classifier and Calibration Back-endmentioning

confidence: 99%

Extended phone log-likelihood ratio features and acoustic-based i-vectors for language recognition

D’Haro

Córdoba

Salamea

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents new techniques with relevant improvements added to the primary system presented by our group to the Albayzin 2012 LRE competition, where the use of any additional corpora for training or optimizing the models was forbidden. In this work, we present the incorporation of an additional phonotactic subsystem based on the use of phone log-likelihood ratio features (PLLR) extracted from different phonotactic recognizers that contributes to improve the accuracy of the system in a 21.4% in terms of C avg (we also present results for the official metric during the evaluation, F act ). We will present how using these features at the phone state level provides significant improvements, when used together with dimensionality reduction techniques, especially PCA. We have also experimented with applying alternative SDC-like configurations on these PLLR features with additional improvements. Also, we will describe some modifications to the MFCC-based acoustic i-vector system which have also contributed to additional improvements. The final fused system outperformed the baseline in 27.4% in C avg .

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“…Every speaker GMM is adapted from a background model using the eigenvoice approach [23]. Given a sequence of feature vectors, 20 speaker factors are estimated for every frame over a 100-frame window and then transformed with the within-class covariance normalization (WCCN) [26] in order to compensate for the intra-session variability. Afterwards, a 10-Gaussian GMM is estimated to model the stream of http://asmp.eurasipjournals.com/content/2012/1/19 speaker factors (as in [12]), where each Gaussian will be assigned to a single speaker.…”

Section: Systemmentioning

confidence: 99%

Speaker diarization of broadcast news in Albayzin 2010 evaluation campaign

Zelenák

Schulz

Hernando

2012

J AUDIO SPEECH MUSIC PROC.

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In this article, we present the evaluation results for the task of speaker diarization of broadcast news, which was part of the Albayzin 2010 evaluation campaign of language and speech technologies. The evaluation data consists of a subset of the Catalan broadcast news database recorded from the 3/24 TV channel. The description of five submitted systems from five different research labs is given, marking the common as well as the distinctive system features. The diarization performance is analyzed in the context of the diarization error rate, the number of detected speakers and also the acoustic background conditions. An effort is also made to put the achieved results in relation to the particular system design features.

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Generalized Linear Kernels for One-Versus-All Classification: Application to Speaker Recognition

Cited by 44 publications

References 4 publications

Face Recognition Using Simplified Probabilistic Linear Discriminant Analysis

Face Recognition Using Simplified Probabilistic Linear Discriminant Analysis

Extended phone log-likelihood ratio features and acoustic-based i-vectors for language recognition

Speaker diarization of broadcast news in Albayzin 2010 evaluation campaign

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