Article thumbnail

Neural Comput & Applic manuscript No. (will be inserted by the editor) Author manuscript, published in "Neural Computing and Applications (2012)" DOI: 10.1007/s00521-012-1079-y Discriminative speaker recognition using Large Margin GMM

By Reda Jourani, Khalid Daoudi Régine, André-obrecht Driss Aboutajdine, R. Jourani, R. André-obrecht, K. Daoudi, R. Jourani and D. Aboutajdine


Abstract 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

Year: 2012
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.