A HIERARCHICAL SYSTEM DESIGN FOR LANGUAGE IDENTIFICATION

摘要：

Token-based approaches have proven quite effective for spoken language identification (LID). Traditionally, Speech utterances are first decoded into token sequences, and then LID tasks are performed on these token sequences by either n-graM language models or support vector machines. In this paper, we propose a hierarchical system design, which utilizes a group of bayesian logistic regression models as score generators. Score generators are then followed by a score merger, which outputs the final identification results. Experiments conducted on the NISR LRE 2007 databases demonstrate that the proposed approach achieves quite competitive performance compared to other traditional token-based methods.

关键词： language identification bayesian logistic regression model hierarchical system design

作者: Haipeng Wang Xiang Xiao Xiang Zhang Jianping Zhang Yonghong Yan

作者单位: ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing, P.R.China ThinkIT Speech Lab, Institute of Acoustics, Chinese Academy of Sciences, Beijing,P.R.China ThinkIT Speech Lab, Institute of Acoustics,Chinese Academy of Sciences, Beijing, P.R.China

会议类型: 国际会议

会议名称: Second International Symposium on Information Science and Engineering(第二届信息科学与工程国际会议)

会议地点: 上海

会议语种:英文

页码: 443-446

在线出版日期: 2009-12-26（万方平台首次上网日期，不代表论文的发表时间）