A bayesian logistic regression approach to spoken language identification
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概要
- 論文の詳細を見る
This paper presents a novel token-based approach for spoken language identification (LID) using bayesian logistic regression model, which takes into account prior distribution for parameters of logistic regression models in order to avoid overfitting. Speech utterances are first decoded into token sequences, and then we design a hierarchical system which utilizes bayesian logistic regression model to perform LID task on these token sequences. Experiments conducted on the NIST LRE 2007 database show that the proposed approach provides quite competitive performance compared to other state-of-the-art token-based approaches.
- The Institute of Electronics, Information and Communication Engineersの論文
著者
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Yan Yonghong
Thinkit Speech Lab Institute Of Acoustics Chinese Academy Of Sciences
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Xiao Xiang
Thinkit Speech Lab. Institute Of Acoustics Chinese Academy Of Sciences
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Zhang Xiang
Thinkit Speech Lab Institute Of Acoustics Chinese Academy Of Sciences
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ZHANG Jianping
ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences
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Yan Yonghong
Thinkit Speech Lab, Institute of Acoustics, Chinese Academy of Sciences
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Wang Haipeng
Thinkit Speech Lab, Institute of Acoustics, Chinese Academy of Sciences
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Xiao Xiang
Thinkit Speech Lab, Institute of Acoustics, Chinese Academy of Sciences
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