Momeni Maryam, Rahmani Mahdiyeh
Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, Iran.
Cogn Neurodyn. 2021 Jun;15(3):453-461. doi: 10.1007/s11571-020-09644-z. Epub 2020 Oct 13.
In recent years, extensive studies have been conducted on the diagnosis of Alzheimer's disease (AD) using the non-invasive speech signal recognition method. In this study, Farsi speech signals were analyzed using the auditory model system (AMS) in order to recognize AD. For this purpose, after the pre-processing of the speech signals and utilizing AMS, 4D outputs as function of time, frequency, rate, and scale range were obtained. The AMS outcomes were averaged in term of time to analyze the rate-frequency-scale for both groups, Alzheimer's and healthy control subjects. Thereafter, the maximum of spectral and temporal modulation and frequency were extracted to classify by the support vector machine (SVM). The SVM achieves higher promising recognition accuracy with compare to prevalent approaches in the field of speech processing. The acceptable results demonstrate the applicability of the proposed algorithm in non-invasive and low-cost recognizing Alzheimer's only using the few extracted features of the speech signal.
近年来，人们对使用非侵入性语音信号识别方法诊断阿尔茨海默病（AD）进行了广泛研究。在本研究中，为了识别AD，使用听觉模型系统（AMS）对波斯语语音信号进行了分析。为此，在对语音信号进行预处理并利用AMS之后，获得了作为时间、频率、速率和尺度范围函数的4D输出。对AMS结果按时间进行平均，以分析阿尔茨海默病组和健康对照组的速率-频率-尺度。此后，提取频谱和时间调制及频率的最大值，通过支持向量机（SVM）进行分类。与语音处理领域的现有方法相比，SVM实现了更高的识别准确率。可接受的结果证明了所提算法在仅使用语音信号的少数提取特征进行非侵入性和低成本阿尔茨海默病识别中的适用性。
