Language Recognition with Word Lattices and Support Vector Machines

Language recognition is typically performed with methods that exploit phonotactics - a phone recognition language modeling (PRLM) system. A PRLM system converts speech to a lattice of phones and then scores a language model. A standard extension to this scheme is to use multiple parallel phone recognizers (PPRLM). In this paper, we modify this approach in two distinct ways. First, we replace the phone tokenizer by a powerful speech-to-text system. Second, we use a discriminative support vector machine for language modeling. Our goals are twofold. First, we explore the ability of a single speech-to-text system to distinguish multiple languages. Second, we fuse the new system with an SVM PRLM system to see if it complements current approaches. Experiments on the 2005 NIST language recognition corpus show the new word system accomplishes these goals and has significant potential for language recognition.

[1]  Douglas A. Reynolds,et al.  Fusing high- and low-level features for speaker recognition , 2003, INTERSPEECH.

[2]  William M. Campbell,et al.  High-level speaker verification with support vector machines , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[3]  Herbert Gish,et al.  Discriminatively trained Language Models using Support Vector Machines for Language Identification , 2006, 2006 IEEE Odyssey - The Speaker and Language Recognition Workshop.

[4]  Samy Bengio,et al.  SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..

[5]  Douglas A. Reynolds,et al.  The SuperSID project: exploiting high-level information for high-accuracy speaker recognition , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[6]  Daniel Povey,et al.  Large scale MMIE training for conversational telephone speech recognition , 2000 .

[7]  Jean-Luc Gauvain,et al.  Language recognition using phone latices , 2004, INTERSPEECH.

[8]  Frederick Jelinek,et al.  Statistical methods for speech recognition , 1997 .

[9]  Marc A. Zissman,et al.  Comparison of : Four Approaches to Automatic Language Identification of Telephone Speech , 2004 .

[10]  William M. Campbell,et al.  Advanced Language Recognition using Cepstra and Phonotactics: MITLL System Performance on the NIST 2005 Language Recognition Evaluation , 2006, 2006 IEEE Odyssey - The Speaker and Language Recognition Workshop.

[11]  Jiří Navrátil.  Automatic Language Identification , 2006 .

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

[13]  Jean-Luc Gauvain,et al.  Discriminative Classifiers for Language Recognition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.