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18th International Conference on Pattern Recognition (ICPR'06) Volume 4
Multi-lingual Phoneme Recognition and Language Identification Using Phonotactic Information
Hong Kong
August 20-August 24
ISBN: 0-7695-2521-0
ASCII Text
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Liang Wang, Eliathamby Ambikairajah, Eric H.C. Choi, "Multi-lingual Phoneme Recognition and Language Identification Using Phonotactic Information," Pattern Recognition, International Conference on, vol. 4, pp. 245-248, 18th International Conference on Pattern Recognition (ICPR'06) Volume 4, 2006.
 
 
BibTex
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@article{ 10.1109/ICPR.2006.823,
author = {Liang Wang and Eliathamby Ambikairajah and Eric H.C. Choi},
title = {Multi-lingual Phoneme Recognition and Language Identification Using Phonotactic Information},
journal ={Pattern Recognition, International Conference on},
volume = {4},
year = {2006},
issn = {1051-4651},
pages = {245-248},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.823},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Pattern Recognition, International Conference on
TI - Multi-lingual Phoneme Recognition and Language Identification Using Phonotactic Information
SN - 1051-4651
SP245
EP248
A1 - Liang Wang,
A1 - Eliathamby Ambikairajah,
A1 - Eric H.C. Choi,
PY - 2006
KW - null
VL - 4
JA - Pattern Recognition, International Conference on
ER -
 
Liang Wang, University of New South Wales, Sydney, Australia
Eliathamby Ambikairajah, University of New South Wales, Sydney, Australia
Eric H.C. Choi, ATP Research Laboratory National ICT Australia, Sydney, Australia
Previous research indicates that automatic language identification systems based on phonotactic information produce the best results compared with other systems based on acoustic or prosodic information. This paper investigates two different approaches that use phonotactic information: Parallel Phoneme Recognition followed by Language Modeling (PPRLM) and multi-lingual PRLM. In the PPRLM approach, we have modified the system by using four different language models with different discounting methods, including the Linear, Absolute, Good- Turning and Witten-Bell. Our results show that the modified PPRLM system with the Witten-Bell discounting outperforms other systems and achieves 75.5% language identification accuracy for the OGITS speech corpus.
Citation:
Liang Wang, Eliathamby Ambikairajah, Eric H.C. Choi, "Multi-lingual Phoneme Recognition and Language Identification Using Phonotactic Information," icpr, vol. 4, pp.245-248, 18th International Conference on Pattern Recognition (ICPR'06) Volume 4, 2006
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