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Karia Ba Mohamed, Morocco

Daoudi K.,French Institute for Research in Computer Science and Automation | Jourani R.,University Paul Sabatier | Jourani R.,Mohammed 5 Agdal University | Andre-Obrecht R.,University Paul Sabatier | Aboutajdine D.,Mohammed 5 Agdal University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decades. They are generally trained using the generative criterion of maximum likelihood estimation. In an earlier work, we proposed an algorithm for discriminative training of GMM with diagonal covariances under a large margin criterion. In this paper, we present a new version of this algorithm which has the major advantage of being computationally highly efficient, thus well suited to handle large scale databases. We evaluate our fast algorithm in a Symmetrical Factor Analysis compensation scheme. We carry out a full NIST speaker identification task using NIST-SRE'2006 data. The results show that our system outperforms the traditional discriminative approach of SVM-GMM supervectors. A 3.5% speaker identification rate improvement is achieved. © 2011 Springer-Verlag. Source


Jourani R.,University Paul Sabatier | Jourani R.,Mohammed 5 Agdal University | Daoudi K.,French Institute for Research in Computer Science and Automation | Andre-Obrecht R.,University Paul Sabatier | Aboutajdine D.,Mohammed 5 Agdal University
European Signal Processing Conference | Year: 2013

Most state-of-the-art speaker recognition systems are partially or completely based on Gaussian mixture models (GMM). GMM have been widely and successfully used in speaker recognition during the last decades. They are traditionally estimated from a world model using the generative criterion of Maximum A Posteriori. In an earlier work, we proposed an efficient algorithm for discriminative learning of GMM with diagonal covariances under a large margin criterion. In this paper, we evaluate the combination of the large margin GMM modeling approach with SVM in the setting of speaker identification. We carry out a full NIST speaker identification task using NIST-SRE'2006 data, in a Symmetrical Factor Analysis compensation scheme. The results show that the two modeling approaches are complementary and that their combination outperforms their single use. © 2013 EURASIP. Source


Jourani R.,CNRS Toulouse Institute in Information Technology | Jourani R.,Mohammed 5 Agdal University | Daoudi K.,French Institute for Research in Computer Science and Automation | Andre-Obrecht R.,CNRS Toulouse Institute in Information Technology | Aboutajdine D.,Mohammed 5 Agdal University
International Conference on Multimedia Computing and Systems -Proceedings | Year: 2011

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decades. They are generally trained using the generative criterion of maximum likelihood estimation. In an earlier work, we proposed an algorithm for discriminative training of GMM with diagonal covariances under a large margin criterion. In this paper, we present a new version of this algorithm which has the major advantage of being computationally highly efficient. The resulting algorithm is thus well suited to handle large scale databases. To show the effectiveness of the new algorithm, we carry out a full NIST speaker verification task using NIST-SRE'2006 data. The results show that our system outperforms the baseline GMM, and with high computational efficiency. © 2011 IEEE. Source


Jourani R.,CNRS Toulouse Institute in Information Technology | Jourani R.,Mohammed 5 Agdal University | Daoudi K.,French Institute for Research in Computer Science and Automation | Andre-Obrecht R.,CNRS Toulouse Institute in Information Technology | Aboutajdine D.,Mohammed 5 Agdal University
Proceedings of the 6th International Conference on Speech Technology and Human-Computer Dialogue, SpeD 2011 | Year: 2011

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decades. They are generally trained using the generative criterion of maximum likelihood estimation. In an earlier work, we proposed an algorithm for discriminative training of GMM with diagonal covariances under a large margin criterion. In this paper, we present a new version of this algorithm which has the major advantage of being computationally highly efficient. The resulting algorithm is thus well suited to handle large scale databases. We carry out experiments on a speaker identification task using NIST-SRE'2006 data and compare our new algorithm to the baseline generative GMM using different GMM sizes. The results show that our system significantly outperforms the baseline GMM in all configurations, and with high computational efficiency. © 2011 IEEE. Source


Jourani R.,CNRS Toulouse Institute in Information Technology | Jourani R.,Mohammed 5 Agdal University | Daoudi K.,French Institute for Research in Computer Science and Automation | Andre-Obrecht R.,CNRS Toulouse Institute in Information Technology | Aboutajdine D.,Mohammed 5 Agdal University
Neural Computing and Applications | Year: 2013

Most state-of-the-art speaker recognition systems are based on discriminative learning approaches. On the other hand, generative Gaussian mixture models (GMM) have been widely used in speaker recognition during the last decades. In an earlier work, we proposed an algorithm for discriminative training of GMM with diagonal covariances under a large margin criterion. In this paper, we propose an improvement of this algorithm, which has the major advantage of being computationally highly efficient, thus well suited to handle large-scale databases. We also develop a new strategy to detect and handle the outliers that occur in the training data. To evaluate the performances of our new algorithm, we carry out full NIST speaker identification and verification tasks using NIST-SRE'2006 data, in a Symmetrical Factor Analysis compensation scheme. The results show that our system significantly outperforms the traditional discriminative support vector machines (SVM)-based system of SVM-GMM supervectors, in the two speaker recognition tasks. © 2012 Springer-Verlag London Limited. Source

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