概率SVM/GMM分类器用于连续语音中非特定人元音识别
Probabilistic SVM/GMM Classifier for Speaker-Independent Vowel Recognition in Continues Speech
摘要：
本文讨论了波斯语元音的自动识别问题。目前的工作重点是新的统计方法识别元音作为音节的基本单位。首先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输入到识别单元。在模式识别中，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两种最流行的技术。当前最先进的系统试图将它们结合在一起，以实现更大的分类能力并提高识别系统的性能。该研究的主要思想是将概率支持向量机和传统的GMM模式分类相结合，并结合语音的一些特征，如带通能量，以获得更好的分类率。这个想法已被解析制定和测试的FarsDat基于元音识别系统。结果显示，识别准确率有了不可思议的提高。测试已经进行了各种建议的元音识别算法和结果进行了比较。
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
