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首页> 外文期刊>International journal of speech technology >Speaker discrimination based on fuzzy fusion and feature reduction techniques
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Speaker discrimination based on fuzzy fusion and feature reduction techniques

机译：基于模糊融合和特征约简技术的说话人识别

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摘要

In this paper, we propose a research work on speaker discrimination using a multi-classifier fusion with focus on feature reduction effects. Speaker discrimination consists in the automatic distinction between two speakers using the vocal characteristics of their speeches. A number of features are extracted using Mel Frequency Spectral Coefficients and then reduced using Relative Speaker Characteristic (RSC) along with the Principal Components Analysis (PCA). Several classification methods are implemented to ensure the discrimination task. Since different classifiers are employed, two fusion algorithms at the decision level, referred to as Weighted Fusion and Fuzzy Fusion, are proposed to boost the classification performances. These algorithms are based on the weighting of the different classifiers outputs. Furthermore, the effects of speaker gender and feature reduction on the speaker discrimination task have been examined too. The evaluation of our approaches was conducted on a subset of Hub-4 Broadcast-News. The experimental results have shown that the speaker discrimination accuracy is improved by 5-15% using the (RSC-PCA) feature reduction. In addition, the proposed fusion methods recorded an improvement of about 10% compared to the individual scores of the classifiers. Finally, we noticed that the gender has an important impact on the discrimination performances.
机译：在本文中，我们提出了使用多分类器融合进行说话人识别的研究工作，重点是特征减少效果。说话者辨别在于利用他们说话的声音特征自动区分两个说话者。使用梅尔频率频谱系数提取许多特征，然后使用相对说话者特征（RSC）和主成分分析（PCA）进行简化。实施了几种分类方法以确保区分任务。由于采用了不同的分类器，提出了两种决策级的融合算法：加权融合和模糊融合，以提高分类性能。这些算法基于不同分类器输出的加权。此外，还研究了说话人性别和特征减少对说话人歧视任务的影响。我们对方法的评估是在Hub-4广播新闻的子集中进行的。实验结果表明，使用（RSC-PCA）功能降低，可以将说话人的辨别精度提高5-15％。另外，与分类器的单个分数相比，所提出的融合方法记录了约10％的改进。最后，我们注意到性别对歧视表现有重要影响。

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