1. Improved Phonotactic Language Recognition Using Collaborated Language Model
- Author
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Jia Liu, Dong Taiqing, Wei-Qiang Zhang, Weihua Zhang, Zhikai Hu, Li Lintao, Zhou Jianhua, Tang Yujian, Wei-Wei Liu, Wang Xiguang, Zhao Peng, and Wu Dong
- Subjects
Phonotactics ,Language identification ,Computer science ,business.industry ,Word error rate ,020206 networking & telecommunications ,02 engineering and technology ,computer.software_genre ,01 natural sciences ,Term (time) ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,Feature (machine learning) ,NIST ,Language model ,Artificial intelligence ,business ,010301 acoustics ,computer ,Natural language processing - Abstract
In this paper an approach to build a collaborated feature supervector is proposed and a collaborated language model (CLM) builded with binary decision tree feature surpervector and N-gram feature supervector is introduced and applied to deal with the problems of language identification tasks such as handling long term contexts and too many parameters. Experiments are carried out on the database of National Institute of Standards and Technology language recognition evaluation 2009 (NIST LRE 2009). The experimental results have confirmed that phonotactic language recognition system using the collaborated language model yields 1.07%, 2.68%, 13.48% in equal error rate (EER), which means 8.54%, 12.70% and 4.60% relative reduction for 30s, 10s, 3s compared to the baseline system, respectively.
- Published
- 2018