Automatic identification of pathological voice quality based on the GRBAS categorization
Automatic identification of pathological voice quality based on the GRBAS categorization
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DOI：
10.1109/apsipa.2017.8282229
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
A. Sasou
中科院分区：
文献类型：
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作者：
A. Sasou
基于声学分析的语音病理自动检测能够对疾病的存在进行非侵入性、低成本和客观的评估，这可能有助于加速和改善对患者的诊断和临床治疗。在本文中，我们专注于自动评估的病理语音质量，通过识别粗糙，呼吸，虚弱，和应变的基础上GRBAS分类的四个属性。该方法采用高阶局部自相关（HLAC）功能，这是计算从激励源信号通过自动拓扑生成的AR-HMM分析，并确定使用前馈神经网络（FFNN）为基础的分类器的四个属性。实验结果表明，在一个说话人识别任务中，平均F测度为87.25%，验证了该方法的可行性。
Acoustic analysis-based automatic detection of voice pathologies enables non-invasive, low-cost and objective assessments of the presence of disorders, which might assist in accelerating and improving the diagnosis and clinical treatment given to patients. In this paper, we focus on the automatic assessment of pathological voice quality by identifying the four attributes of Roughness, Breathiness, Asthenia, and Strain based on the GRBAS categorization. The proposed method adopts higher-order local auto-correlation (HLAC) features, which are calculated from the excitation source signal obtained by an automatic topology-generated AR-HMM analysis, and identifies the four attributes using a feed-forward neural network (FFNN)-based classifier. In the experiments, an average F-measure of 87.25% was obtained for a speaker- based identification task, which confirms the feasibility of the proposed method.