基于非线性方法的病理嗓音识别研究

提出了一种基于非线性特性来提取声带小结类病理嗓音的特征参数的识别方法.首先通过滤波分割的方法,分两个通道处理语音信号,低频部分采用符合人耳听觉特性的巴克滤波器组进行信号重构并提取语音特征,高频部分采用非线性动力学的最大李雅普诺夫指数来描述,最后整合为语音特征序列并进行语音识别.采用美国MEEI公司的病理嗓音数据库进行识别实验.实验结果表明,这种方法能够有效地提高病理嗓音的识别率,达到99.4％的识别率.

Pathological Voice Recognition based on Nonlinear Method

This paper proposes a special identification method based on nonlinear characteristics to extract the characteristic parameters of the vocal nodules class of pathological voice. Firstly, by using the method of filtering segmentation, these voice signals are divided into two-channel to process. The low-frequency voice signals use the Bark filter bank meeting the human auditory characteristics to reconstruct the signals and extract the speech features, while the high-frequency ones use the largest Lyapunov exponent based on the nonlinear dynamics. Finally, The two should be integrated into a sequence of speech features for voice recognition. Recognition experiment is based on the database of pathological voices of the U.S. MEEI＇s company. The experimental results indicate that this method can effectively improve the recognition rate of pathological voice, making the recognition rate up to 99.4%.