A Multi-Modal Feature Embedding Approach to Diagnose Alzheimer Disease from Spoken Language

Autor:	Zargarbashi, S. Soroush Haj, Babaali, Bagher
Rok vydání:	2019
Předmět:	Computer Science - Machine Learning Computer Science - Computation and Language Electrical Engineering and Systems Science - Audio and Speech Processing Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Introduction: Alzheimer's disease is a type of dementia in which early diagnosis plays a major rule in the quality of treatment. Among new works in the diagnosis of Alzheimer's disease, there are many of them analyzing the voice stream acoustically, syntactically or both. The mostly used tools to perform these analysis usually include machine learning techniques. Objective: Designing an automatic machine learning based diagnosis system will help in the procedure of early detection. Also, systems, using noninvasive data are preferable. Methods: We used are classification system based on spoken language. We use three (statistical and neural) approaches to classify audio signals from spoken language into two classes of dementia and control. Result: This work designs a multi-modal feature embedding on the spoken language audio signal using three approaches; N-gram, i-vector, and x-vector. The evaluation of the system is done on the cookie picture description task from Pitt Corpus dementia bank with the accuracy of 83:6 Comment: 14 pages, 4 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1910.00330 Zobrazit plný text záznamu View this record from Arxiv