Sarria-Paja M, Castellanos-Dominguez G, Delgado-Trejos E
Research Center in Instituto Tecnológico Metropolitano, Medellín Colombia.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:4674-7. doi: 10.1109/IEMBS.2010.5626408.
This paper presents a new approach that improves discriminative training criterion for Hidden Markov Models, and is oriented to pathological voice identification. This technique is aimed at maximizing the Area under the Curve of a receiver operating characteristic curve by adjusting the model parameters using as objective function the Mahalanobis distance and the distance between means of the underlying probability density functions associated with each class. The results show that the proposed technique significantly outperforms the accuracy in a classification system compared with other training criteria. Results are provided using the MEEIVL voice disorders database.
本文提出了一种改进隐马尔可夫模型判别训练准则的新方法，该方法面向病理性语音识别。该技术旨在通过使用马氏距离和与每个类别相关的潜在概率密度函数均值之间的距离作为目标函数来调整模型参数，从而最大化接收器操作特性曲线的曲线下面积。结果表明，与其他训练准则相比，该技术在分类系统中的准确率显著更高。使用MEEIVL语音障碍数据库给出了结果。
