Alzheimer Speech Signal Analysis of Persian speaking Alzheimer's patients

Alzheimer's is a type of brain dementia that gradually reduces mental abilities of the patient. The lack of memory, decision-making disorder, and mistakes in choosing the correct vocabulary are the early symptoms of Alzheimer's disease. Therefore, extensive studies have been conducted on the diagnosis of Alzheimer's disease using the non-invasive speech signal recognition method. Identifying of Alzheimer's disease is dependent on culture and language, speech content, gender, age, accent, and many other factors. Therefore, Alzheimer's speech signal has been studied in various languages. The purpose of this paper is to recognize Alzheimer's patients from healthy people by the use of their speech signal processing in Persian using the combination of time, frequency, and frequency-temporal features. In this paper, after pre-processing, the speech features extracted using the wavelet packet as a frequency-temporal feature next to Mel frequency Cepstral coefficients, zero crossing rate, spectral roll off, band width, root mean square and spectral centroid frequency. Finally, the extracted features have been classified by the support vector machine which achieves recognition precision of 96% on Persian healthy and Alzheimer's speaker experiments. The acceptable results demonstrate the applicability of the proposed non-invasive and low-cost algorithm for the diagnosis of Persian-speaking Alzheimer's patients.