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首页> 外文会议>2010 18th Iranian Conference on Electrical Engineering >Improvement on automatic speaker gender identification using classifier fusion
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Improvement on automatic speaker gender identification using classifier fusion

机译：利用分类器融合对说话人性别自动识别的改进

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In this paper, a two layer classifier fusion technique is proposed for automatic gender identification (AGI). The first layer is an acoustic classification layer for mapping MFCC acoustic feature space to score space. In this layer, a divisive clustering is proposed for dividing the speakers from each gender to some classes of speakers having similar vocal articulatory. The second layer is a back-end classifier that receives the vectors of fused likelihood scores from the first layer. GMM, SVM and MLP classifiers were evaluated in the middle and back-end layers. 96.53% gender classification accuracy was obtained on OGI multilingual corpus which is much better than the performance obtained by traditional AGI methods.
机译：本文提出了一种用于自动性别识别的两层分类器融合技术。第一层是用于将MFCC声学特征空间映射到得分空间的声学分类层。在这一层中，提出了一个划分性的聚类，用于将说话人从每个性别划分为具有相似发音的说话人。第二层是后端分类器，它从第一层接收融合似然分数的向量。在中间层和后端层评估了GMM，SVM和MLP分类器。 OGI多语言语料库的性别分类准确率达到96.53％，远优于传统AGI方法获得的表现。

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