语音障碍分类的统计建模

5th International Conference on Design & Technology of Integrated Systems in Nanoscale Era Pub Date : 2010-03-23 DOI:10.1109/DTIS.2010.5487588

Assia Ghelis, M. Guerti, C. Fredouille

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引用次数: 0

摘要

我们的工作目标是开发一个自动系统，通过使用基于高斯混合模型(GMM)的统计建模语音信号来评估阿拉伯语/法语语音分类，这是目前最先进的说话人识别方法。演讲者在安纳巴大学医院中心(CHU)的耳鼻喉科进行，由8名医学专家组成的小组在场。实验结果表明，该自动识别系统能够在法语或阿拉伯语中识别出发音困难的说话者，并且表现良好。

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Statistical modeling for dysphonic classification

The objective of our work is to develop an automatic system to evaluate Arabic/French dysphonic classification by modeling speech signals using a statistical modeling based on a Gaussian Mixture Model (GMM), which is state of art in speaker recognition. Speakers were conducted at Annaba University Hospital center (CHU) in the ENT service in the presence of a group composed of 8 medical specialists. Results of the experiment show that an automatic system is able to identify dysphonic speakers with an acceptable performance either in French or in Arabic language.

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5th International Conference on Design & Technology of Integrated Systems in Nanoscale Era

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