Effective Arabic Dialect Classification Using Diverse Phonotactic Models

We study the effectiveness of recently developed language recognition techniques based on speech recognition models for the discrimination of Arabic dialects. Specifically, we investigate dialect-specific and cross-dialectal phonotacticmodels, using both languagemodels and support vector machines (SVMs). Techniques are evaluated both alone and in combination with a cepstral system with joint factor analysis (JFA), using a fourdialect data set employing30-second telephone speech samples. We find good complementarity from different features andmodeling paradigms, and achieve 2% average equal error rate for pairwise classification.

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

[2]  Andreas Stolcke,et al.  Improving Language Recognition with Multilingual Phone Recognition and Speaker Adaptation Transforms , 2010, Odyssey.

[3]  Chao Huang,et al.  Automatic accent identification using Gaussian mixture models , 2001, IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01..

[4]  Andreas Stolcke,et al.  Detecting nonnative speech using speaker recognition approaches , 2008, Odyssey.

[5]  Driss Matrouf,et al.  A straightforward and efficient implementation of the factor analysis model for speaker verification , 2007, INTERSPEECH.

[6]  Nizar Habash,et al.  Spoken Arabic Dialect Identification Using Phonotactic Modeling , 2009, SEMITIC@EACL.

[7]  Lukás Burget,et al.  Comparison of scoring methods used in speaker recognition with Joint Factor Analysis , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[8]  Yi Su,et al.  Accent detection and speech recognition for Shanghai-accented Mandarin , 2005, INTERSPEECH.

[9]  Marc A. Zissman,et al.  Automatic dialect identification of extemporaneous conversational, Latin American Spanish speech , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[10]  Daniel P. W. Ellis,et al.  Dialect and Accent Recognition Using Phonetic-Segmentation Supervectors , 2011, INTERSPEECH.

[11]  Patrick Kenny,et al.  Factor analysis simplified [speaker verification applications] , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[12]  William M. Campbell,et al.  Experiments with Lattice-based PPRLM Language Identification , 2006, 2006 IEEE Odyssey - The Speaker and Language Recognition Workshop.

[13]  Andreas Stolcke,et al.  Improved phonetic speaker recognition using lattice decoding , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[14]  William M. Campbell,et al.  Phonetic Speaker Recognition with Support Vector Machines , 2003, NIPS.

[15]  Lukás Burget,et al.  BUT language recognition system for NIST 2007 evaluations , 2008, INTERSPEECH.

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

[17]  M. Zissman.  Automatic Language Identification of Telephone Speech , 1993 .

[18]  Douglas A. Reynolds,et al.  Approaches to language identification using Gaussian mixture models and shifted delta cepstral features , 2002, INTERSPEECH.

[19]  Jean-Luc Gauvain,et al.  Context-dependent phone models and models adaptation for phonotactic language recognition , 2008, INTERSPEECH.