结合模型混淆度和BIC准则的语种识别精细建模方法  

Language Identification Based on Combination of Model Confusion and BIC Criteria

作　　者：徐颖[1] 宋彦[1] 戴礼荣[1] 

机构地区：[1]中国科学技术大学电子工程与信息科学系,合肥230027

出　　处：《数据采集与处理》2011年第5期573-578,共6页Journal of Data Acquisition and Processing

基　　金：安徽省自然科学基金(090412056)资助项目

摘　　要：提出了一种基于语种模型混淆度的模型参数估计方法,并结合贝叶斯信息准则(Bayesian informationcriterion,BIC)来进行模型的选取,避免了大量标注信息的需求。在NIST-07语种识别30,10和3s的测试任务中,分别给出了在最大似然(Maximum likelihood,ML)准则和最大互信息(Maximum mutual information,MMI)准则下性能比较,所提出的方法相对于基线系统,性能都有明显的提升,而且达到了利用标注信息进行细化建模相同的水平。In language identification, the detailed annotations can make the model parameters estimate more accurately, but these annotations are difficult to be acquired. This paper proposes a kind of model parameters estimation method based on language model confusion, combin- ing Bayesian information criterion （BIC） for model selection, avoid acquiring large annotations. In NIST07 language recognition for 30, 10 and 3 s test tasks, we presented performance comparison in the maximum likelihood （ML） criterion and the maximum mutual information （MMI） criterion, our proposed method have significantly improvement, and reach the same level as using annotations.

关 键 词：语种识别 贝叶斯信息准则 模型混淆度 高斯混合模型 

分 类 号：TN912.34[电子电信—通信与信息系统]