结合差分演化和逻辑回归的构音障碍自动识别方法

针对传统的构音障碍诊断方法存在耗时高、成本高等问题,提出一种构音障碍语音的计算机自动识别方法。结合Gammatone频率倒谱系数(Gammatone Frequency Cepstrum Coefficients, GFCC)与常用声学特征形成组合声学特征,应用差分演化算法进行特征选择,并使用逻辑回归分类器对构音障碍语音进行识别。将Torgo构音障碍语音数据库分成3个语音子集,分别是非词、短词语、限制句子集,提取24维GFCC和37维常用的声学特征构成组合声学特征,最后使用差分演化算法和逻辑回归分类器进行分类识别。实验表明:使用差分演化算法可以有效选择出具有更佳识别能力的特征,从而显著提高构音障碍识别率。在非词子集上的实验准确率达到98.18%,召回率为98.3%,精确率为98.3%。

Automatic Recognition of Dysarthria Based on Differential Evolution and Logistic Regression

Aiming at the problems of high time consuming and cost in traditional diagnosis of dysarthria speech, a computer automatic recognition method for dysarthria is proposed. Combining the Gammatone Frequency Cepstrum Coefficients (GFCC) with the common acoustic features to form a combined acoustic feature, a differential evolution algorithm is applied for feature selection, and a logistic regression classifier is used to identify the dysarthria speech. The Torgo database is divided into three subsets, which are non-words, short words, restricted sentence. 24-dimensional GFCC and 37-dimensional commonly used acoustic features are extracted to form combined acoustic features. Finally, differential evolution algorithm and logistic regression classifier are used for identificaiton of dysarthria. Experiments show that the differential evolution algorithm can effectively select feature subsets with better ability to distinguish dysarthria and healthy speech, which can significantly improve performance in the classification of dysarthria. The experiment on non-word subsets achieves 98.18% of accuracy, 98.3% of recall, and 98.3% of precision.