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基于流形正则化极限学习机的语种识别系统

DOI: 10.16383/j.aas.2015.c140916, PP. 1680-1685

Keywords: 语种识别,极限学习机,流形学习,支持向量机

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Abstract:

?支持向量机(Supportvectormachine,SVM)在语种识别中已经起到了重要的作用.近些年来,极限学习机(Extremelearningmachine,ELM)在很多领域取得了成功的应用.相比于SVM,ELM最大的优点在于极易实现、训练速度快,而且通常可以取得与SVM相近甚至优于SVM的识别性能.鉴于ELM这些优异的特点,本文将ELM引入到语种识别中,并针对ELM由于随机初始化模型参数所带来的潜在问题,提出了流形正则化极限学习机(Manifoldregularizedextremelearningmachine,MRELM)算法.实验结果表明,在高斯超矢量(Gaussiansupervector,GSV)特征空间上,相对于SVM基线系统,该算法对30秒语音的识别性能有明显的提升.同时该算法也可以成功地应用到i-vector特征空间中,取得与当前主流的打分算法相近的识别性能.

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