基于语音信号时频特征融合的帕金森病检测方法  

Parkinson′s Disease Detection Method Based on Time-frequency Feature Fusion of Speech Signals

作　　者：王晨哲 季薇[2] 郑慧芬 李云[1] WANG Chenzhe;JI Wei;ZHENG Huifen;LI Yun(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Communications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Geriatric Hospital of Nanjing Medical University,Nanjing 210009,China)

机构地区：[1]南京邮电大学计算机学院,江苏南京210023 [2]南京邮电大学通信与信息工程学院,江苏南京210003 [3]南京医科大学附属老年医院,江苏南京210009

出　　处：《郑州大学学报（理学版）》2025年第1期53-60,共8页Journal of Zhengzhou University:Natural Science Edition

基　　金：江苏省高校基础科学(自然科学)重大项目(21KJA520003)。

摘　　要：发音障碍是帕金森病的早期症状之一。近年来,基于语音信号的帕金森病检测的研究大多采用梅尔刻度下的相关语音特征与深度神经网络模型相结合的方法。然而,现有的模型无法充分关注语音信号的全局时序信息,且梅尔刻度特征在准确表征帕金森病的病理信息方面效果有限。为此,提出了一种基于语音时频特征融合的帕金森病检测方法。首先,提取语音的梅尔频率倒谱系数,并将其作为模型的输入。接着,在已有的S-vectors模型中引入Conformer编码器模块,以提取语音的时域全局特征。最后,将与帕金森病语音检测相关的频域全局特征嵌入时域特征中进行时频信息融合,以实现帕金森病语音检测。在公开帕金森病语音数据集和自采语音数据集上验证了方法的有效性。Dysphonia is one of the earliest symptoms of Parkinson′s disease(PD).In recent years,many studies on the detection of PD based on speech signals used deep neural network models combined with Mel Scale features.However,existing models could adequately focus on the global time-series information of speech signals.And Mel Scale features had limited effectiveness in accurately characterizing the pathological information of PD.To solve the above problems,a speech detection method for PD was proposed based on time-frequency feature fusion.Firstly,Mel frequency cepstrum coefficients(MFCC)were extracted from speech signals and used as the input data for subsequent models.Then,encoder module of Conformer was introduced into the S-vectors model to extract speech global features in time domain.Finally,global features in frequency domain,related to speech detection of PD,were embedded into the time-domain features to fuse the time-frequency information for PD detection ultimately.The effectiveness of the proposed model was verified respectively on a public PD dataset and a self-collected speech dataset.

关 键 词：帕金森病 梅尔频率倒谱系数 S-vectors CONFORMER 时频特征融合 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]