使用模式识别方法检测声带疾病
Vocal folds disorder detection using pattern recognition methods.
作者信息
Wang Jianglin, Jo Cheolwoo
机构信息
SASPL, Changwon National University, Changwon, Korea 641-773.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:3253-6. doi: 10.1109/IEMBS.2007.4353023.
Diagnosis of pathological voice is one of the most important issues in biomedical applications of speech technology. This study focuses on the classification of pathological voice using the HMM(Hidden Markov Model), the GMM(Gaussian Mixture Model) and a SVM (Support Vector Machine), and then compares the results to work done previously using an ANN (Artificial Neural Network). Speech data were collected from those without and those with vocal disorders. Normal and pathological speech data were mixed in out experiment. Six characteristic parameters (Jitter, Shimmer, NHR, SPI, APQ and RAP) were chosen. Then the pattern recognition methods (HMM, GMM and SVM) were used to distinguish the mixed data into categories of normal and pathological speech. We found that the GMM-based method can give us superior classification rates compared to the other classification methods.
病理性嗓音的诊断是语音技术生物医学应用中最重要的问题之一。本研究着重于使用隐马尔可夫模型（HMM）、高斯混合模型（GMM）和支持向量机（SVM）对病理性嗓音进行分类，然后将结果与之前使用人工神经网络（ANN）所做的工作进行比较。语音数据收集自无嗓音障碍者和有嗓音障碍者。在我们的实验中，正常语音数据和病理性语音数据混合在一起。选择了六个特征参数（抖动、闪烁、谐噪比、标准化噪声能量、谐波峰值商和相对平均扰动）。然后使用模式识别方法（HMM、GMM和SVM）将混合数据区分为正常语音和病理性语音类别。我们发现，与其他分类方法相比，基于GMM的方法能给出更高的分类准确率。
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