基于语音停顿度和平坦度的注意缺陷与多动障碍自动检测算法

doi:10.11772/j.issn.1001-9081.2021071213

《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (9): 2917-2925.DOI: 10.11772/j.issn.1001-9081.2021071213

所属专题：多媒体计算与计算机仿真

• 多媒体计算与计算机仿真 • 上一篇下一篇

基于语音停顿度和平坦度的注意缺陷与多动障碍自动检测算法

李国中¹, 崔娅¹, 俄木依欣¹, 何凌¹, 李元媛²(), 熊熙³

^1.四川大学电气工程学院，成都 610065
^2.四川大学华西医院心理卫生中心，成都 610065
^3.成都信息工程大学网络空间安全学院，成都 610225

收稿日期:2021-07-13 修回日期:2021-11-14 接受日期:2021-11-17 发布日期:2022-09-19 出版日期:2022-09-10
通讯作者: 李元媛
作者简介:李国中（1994—），男，河南商丘人，硕士研究生，主要研究方向：自然语言分析、机器学习、语音信号处理、医学信号处理；
崔娅（2001—），女，重庆人，主要研究方向：语音特征处理、医学信号处理；
俄木依欣（2000—），女，四川乐山人，主要研究方向：医学信号处理、医学图像处理；
何凌（1981—），女，四川成都人，副教授，博士，主要研究方向：医学信号处理、医学图像处理；
李元媛（1984—），女，辽宁大连人，副教授，博士，主要研究方向：临床数据挖掘、医学信息抽取； guojipangxie@126.com
熊熙（1983—），男，四川成都人，副教授，博士，CCF会员，主要研究方向：信息抽取、自然语言分析、社会计算。
基金资助:
国家自然科学基金资助项目(81901389);四川省科技计划项目(2019YFS0236)

Automatic detection algorithm for attention deficit/hyperactivity disorder based on speech pause and flatness

Guozhong LI¹, Ya CUI¹, Yixin EMU¹, Ling HE¹, Yuanyuan LI²(), Xi XIONG³

^1.College of Electrical Engineering，Sichuan University，Chengdu Sichuan 610065，China
^2.Mental Health Center，West China Hospital of Sichuan University，Chengdu Sichuan 610065，China
^3.School of Cyberspace Security，Chengdu University of Information Technology，Chengdu Sichuan 610025，China

Received:2021-07-13 Revised:2021-11-14 Accepted:2021-11-17 Online:2022-09-19 Published:2022-09-10
Contact: Yuanyuan LI
About author:LI Guozhong， born in 1994， M. S. candidate. His research interests include natural language analysis， machine learning， speech signal processing， medical signal processing.
CUI Ya， born in 2001. Her research interests include speech feature processing， medical signal processing.
EMU Yixin， born in 2000. Her research interests include medical signal processing， medical image processing.
HE Ling， born in 1981， Ph. D.， associate professor. Her research interests include medical signal processing， medical image processing.
XIONG Xi， born in 1983， Ph. D.， associate professor. His research interests include information extraction， natural language analysis， social computing.
Supported by:
National Natural Science Foundation of China(81901389);Science and Technology Program of Sichuan Province(2019YFS0236)

摘要/Abstract

摘要：

针对注意缺陷与多动障碍（ADHD）临床诊断主要依靠医生主观评估，缺乏客观辅助依据的问题，提出了一种基于语音停顿度和平坦度的ADHD自动检测算法。首先，通过频带差能熵积（FDEEP）参数自动定位语音有话区间，并提取停顿度特征；然后，使用变换平均幅度平方差（TAASD）参数计算语音倍频率，并提取平坦度特征；最后，结合融合特征和支持向量机（SVM）分类器来实现ADHD的自动识别。实验共采集了17位正常对照组儿童和37位ADHD患儿的语音样本。实验结果表明，所提算法能自动检测正常儿童和ADHD患儿，识别正确率为91.38%。

关键词: 注意缺陷与多动障碍, 频带差能熵积, 停顿度, 变换平均幅度平方差, 平坦度

Abstract:

The clinicians diagnose Attention Deficit/Hyperactivity Disorder （ADHD） mainly based on on their subjective assessment， which lacks objective criteria to assist. To solve this problem， an automatic detection algorithm for ADHD based on speech pause and flatness was proposed. Firstly， the Frequency band Difference Energy Entropy Product （FDEEP） parameter was used to automatically locate the segment with voice from the speech and extract the speech pause features. Then， Transform Average Amplitude Squared Difference （TAASD） parameter was presented to calculate the voice multi-frequency and extract the flatness features. Finally， fusion features and the Support Vector Machine （SVM） classifier were combined to realize the automatic recognition of ADHD. The speech samples of the experiment were collected from 17 normal control children and 37 children with ADHD. Experimental results show that the proposed algorithm can effectively discriminate the normal children and children with ADHD， with an accuracy of 91.38%.

Key words: Attention Deficit/Hyperactivity Disorder (ADHD), Frequency band Difference Energy Entropy Product (FDEEP), pause, Transform Average Amplitude Squared Difference (TAASD), flatness

中图分类号:

TP391.1

李国中, 崔娅, 俄木依欣, 何凌, 李元媛, 熊熙. 基于语音停顿度和平坦度的注意缺陷与多动障碍自动检测算法[J]. 计算机应用, 2022, 42(9): 2917-2925.

Guozhong LI, Ya CUI, Yixin EMU, Ling HE, Yuanyuan LI, Xi XIONG. Automatic detection algorithm for attention deficit/hyperactivity disorder based on speech pause and flatness[J]. Journal of Computer Applications, 2022, 42(9): 2917-2925.

图/表 10

图1 基于语音停顿度和倍频率平坦度的注意缺陷与多动障碍自动识别算法流程

Fig. 1 Flowchart of automatic recognition algorithm for attention deficit/hyperactivity disorder based on speech pause and multi-frequency flatness

图2 基于语音FDEEP参数的停顿度声学特征提取算法流程

Fig. 2 Flowchart of acoustic pause feature extraction algorithm based on speech FDEEP parameter

图3 基于语音TAASD参数的平坦度声学特征提取算法流程

Fig. 3 Flowchart of acoustic flatness feature extraction algorithm based on speech TAASD parameters

图4 ADHD患儿与正常对照组的语音信号倍频率

Fig. 4 Speech signal multi-frequency of ADHD patient and normal control

表1 ADHD患者与正常对照组的语音样本子数据集

Tab. 1 Speech sample sub datasets of ADHD patients and normal controls

数据集序号	儿童类别及个数	语音样本数
1	NM17和NADHD16	99
2	NM17和RADHD21	114
3	NADHD16和RADHD21	111
4	NM17和ADHD37	162

表2 本文算法的ADHD自动检测结果 ( %)

Tab. 2 ADHD automatic detection results of the proposed algorithm

数据集序号	停顿度特征+平坦度特征			停顿度特征			平坦度特征
数据集序号	正确率	特异性	灵敏度	正确率	特异性	灵敏度	正确率	特异性	灵敏度
1	91.38	93.84	89.38	92.76	90.06	95.68	90.69	88.73	92.57
2	74.70	78.38	80.65	56.36	44.13	67.03	81.82	83.13	80.59
3	90.90	89.47	92.86	84.68	81.34	87.10	90.63	92.85	88.89
4	80.41	84.16	72.78	66.67	71.89	85.53	80.21	66.23	86.81

图5 语音停顿度特征箱线图

Fig. 5 Box plots of speech pause features

图6 倍频率平坦度特征箱线图

Fig. 6 Box plots of multi- frequency flatness features

表3 显著性分析

Tab. 3 Significance analysis

本文提取的语音特征	显著性水平（p）
最长停顿时长	1.392 9E-19
平均停顿时长	2.944 3E-16
停顿时间占比	2.657 5E-12
倍频率方差	1.028 2E-15
倍频率离散系数	6.284 7E-11
倍频率峰度	6.010 0E-23

表4 本文提取特征与传统病理语音特征自动检测ADHD的识别结果 (%)

Tab. 4 Recognition results of ADHD automatic detection of features extracted in this paper and traditional pathological voice features

语音特征类别	正确率	特异性	灵敏度
短时能量^［44-45］	81.11	82.82	74.78
MFCC^［41-43］	72.76	71.33	78.57
共振峰^［35-36］	62.50	69.23	54.55
基频^［47-49］	82.76	85.19	66.67
本文提出的停顿度和平坦度特征	91.38	93.84	89.38

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基于语音停顿度和平坦度的注意缺陷与多动障碍自动检测算法

Automatic detection algorithm for attention deficit/hyperactivity disorder based on speech pause and flatness

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