Language Recognition via i-vectors and Dimensionality Reduction

In this paper, a new language identification system is presented based on the total variability approach previously developed in the field of speaker identification. Various techniques are employed to extract the most salient features in the lower dimensional i-vector space and the system developed results in excellent performance on the 2009 LRE evaluation set without the need for any post-processing or backend techniques. Additional performance gains are observed when the system is combined with other acoustic systems.

[1]  Patrick Kenny,et al.  A Study of Interspeaker Variability in Speaker Verification , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[2]  Lukás Burget,et al.  Discriminative Training Techniques for Acoustic Language Identification , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[3]  Lukás Burget,et al.  Discriminative training and channel compensation for acoustic language recognition , 2008, INTERSPEECH.

[4]  Geoffrey E. Hinton,et al.  Neighbourhood Components Analysis , 2004, NIPS.

[5]  Patrick Kenny,et al.  Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification , 2009, INTERSPEECH.

[6]  Lukás Burget,et al.  Discriminative acoustic language recognition via channel-compensated GMM statistics , 2009, INTERSPEECH.

[7]  Patrick Kenny,et al.  Front-End Factor Analysis for Speaker Verification , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  William M. Campbell A covariance kernel for svm language recognition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[10]  Patrick Kenny,et al.  Eigenvoice modeling with sparse training data , 2005, IEEE Transactions on Speech and Audio Processing.

[11]  Douglas E. Sturim,et al.  The MITLL NIST LRE 2009 language recognition system , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[12]  Andreas Stolcke,et al.  Within-class covariance normalization for SVM-based speaker recognition , 2006, INTERSPEECH.

[13]  William M. Campbell,et al.  Support vector machines for speaker and language recognition , 2006, Comput. Speech Lang..

[14]  William M. Campbell,et al.  Channel compensation for SVM speaker recognition , 2004, Odyssey.