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摘要翻译：
本文讨论了波斯语元音自动识别中存在的问题。本文主要研究一种新的统计识别元音的方法，元音是音节的基本单位。首先，我们描述了一个元音检测系统，然后简要讨论了如何将检测到的元音送入识别单元。在模式识别方面，支持向量机(SVM)作为判别分类器和高斯混合模型(GMM)作为生成模型分类器是目前最流行的两种分类器。目前的系统试图将它们结合在一起，以获得更大的分类能力，提高识别系统的性能。研究的主要思想是将概率支持向量机和传统的GMM模式分类结合语音的带通能量等特征，以获得更好的分类率。这一思想已经在一个基于FarsDat的元音识别系统上进行了分析和测试。结果显示，识别准确率有了难以想象的提高。对提出的各种元音识别算法进行了测试，并对测试结果进行了比较。
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英文标题：
《Probabilistic SVM/GMM Classifier for Speaker-Independent Vowel
Recognition in Continues Speech》
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作者：
Mohammad Nazari, Abolghasem Sayadiyan, SeyedMajid Valiollahzadeh
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最新提交年份：
2008
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Multimedia 多媒体
分类描述：Roughly includes material in ACM Subject Class H.5.1.
大致包括ACM学科类H.5.1中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
In this paper, we discuss the issues in automatic recognition of vowels in Persian language. The present work focuses on new statistical method of recognition of vowels as a basic unit of syllables. First we describe a vowel detection system then briefly discuss how the detected vowels can feed to recognition unit. According to pattern recognition, Support Vector Machines (SVM) as a discriminative classifier and Gaussian mixture model (GMM) as a generative model classifier are two most popular techniques. Current state-ofthe- art systems try to combine them together for achieving more power of classification and improving the performance of the recognition systems. The main idea of the study is to combine probabilistic SVM and traditional GMM pattern classification with some characteristic of speech like band-pass energy to achieve better classification rate. This idea has been analytically formulated and tested on a FarsDat based vowel recognition system. The results show inconceivable increases in recognition accuracy. The tests have been carried out by various proposed vowel recognition algorithms and the results have been compared.
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PDF链接：
https://arxiv.org/pdf/0812.2411
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