基于时频混合域局部统计的帕金森病语音障碍分析方法研究

针对帕金森病语音检测问题,本文提出了一种基于时频混合域局部统计的帕金森病语音障碍分析方法。该方法首先将语音信号从时域转化为时频混合域,即进行时频化表示。在时频化表示方法中将语音信号进行分帧处理,再将每帧的语音进行傅里叶变换,通过计算得到能量谱,并将能量谱通过映射关系映射到图像空间进行可视化;其次统计信号每个能量数据在时间轴上和频率轴上的差分值,根据差分值计算该能量的梯度统计特征,用梯度统计特征来表示其不同时域与频域的能量值的突变情况;最后利用KNN分类器对提取的梯度统计特征进行分类。本文在不同的帕金森病语音数据集上进行实验,发现本文所提取的梯度统计特征在分类时有更强的聚类性。与基于传统特征与深度学习特征的分类结果相比,本文所提取的梯度统计特征在分类准确率、特异性和灵敏性上均优于前二者。实验证明了本文所提出的梯度统计特征在帕金森病语音分类诊断中的可行性。

Parkinson’s disease diagnosis based on local statistics of speech signal in time-frequency domain

For speech detection in Parkinson’s patients,we proposed a method based on time-frequency domain gradient statistics to analyze speech disorders of Parkinson’s patients.In this method,speech signal was first converted to time-frequency domain(time-frequency representation).In the process,the speech signal was divided into frames.Through calculation,each frame was Fourier transformed to obtain the energy spectrum,which was mapped to the image space for visualization.Secondly,deviations values of each energy data on time axis and frequency axis was counted.According to deviations values,the gradient statistical features were used to show the abrupt changes of energy value in different time-domains and frequency-domains.Finally,KNN classifier was applied to classify the extracted gradient statistical features.In this paper,experiments on different speech datasets of Parkinson’s patients showed that the gradient statistical features extracted in this paper had stronger clustering in classification.Compared with the classification results based on traditional features and deep learning features,the gradient statistical features extracted in this paper were better in classification accuracy,specificity and sensitivity.The experimental results show that the gradient statistical features proposed in this paper are feasible in speech classification diagnosis of Parkinson’s patients.