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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79012
標題: | 基於神經心理測驗利用遞歸神經網路與特徵序列所開發之阿茲海默症語音評估系統 A Speech Assessment System for Alzheimer’s Disease Based on Neuropsychological Tests Using a Novel Feature Sequence Design and Recurrent Neural Network |
作者: | Yi-Wei Chien 簡易緯 |
指導教授: | 傅立成 |
關鍵字: | 阿茲海默症,評估系統,語音分析,特徵序列,遞歸神經網路, Alzheimer’s Disease,assessment system,speech analysis,feature sequence,Recurrent Neural Network, |
出版年 : | 2018 |
學位: | 碩士 |
摘要: | 阿茲海默症以及其他失智症已成為全世界的第七大死因,由於目前還無有效的治療方法,如何及早診斷並提供介入照顧便成為最重要的課題。相較於腦影像、血液檢查等高成本又繁瑣的方法,本研究的目標就是為社會提供低成本卻方便有效的檢驗服務,透過分析能夠充分反映說話者認知功能的語音訊號,建立一個阿茲海默症評估系統。現今與阿茲海默症偵測與評估的相關研究主要皆依賴於系統設計者設計大量的特徵擷取演算法,再透過統計的檢定選出具有顯著差異的特徵。有別於過去繁複且沒有效率的系統設計,本研究提出一個特徵序列的表示方法,並搭配資料驅動的機器學習模型,來達成區辨一段語音訊號是否有阿茲海默症特性的目的。同時,此系統也能夠達到全自動化的運作,不需要任何人員的介入與操作,使本系統更適合的被大量布建於各式場所。本研究蒐集了150 筆語音資料,並透過一系列的實驗來進行驗證,最終在ROC曲線面積的指標中達到0.969 的結果,如此表現非常具有潛力超越現今最佳的研究。 Alzheimer disease and other dementias have become the 7th cause of death worldwide. Still lacking a cure, an early detection of the disease in order to provide the best intervention is crucial. To develop an assessment system for the general public, speech analysis is the optimal solution since it reflects the speaker’s cognitive skills abundantly and data collection is relatively inexpensive compared with brain imaging, blood testing, etc. While most of the existing literature extracted statistics-based features and relied on a feature selection process, we have proposed a novel Feature Sequence representation and utilized a data-driven approach, namely, the recurrent neural network to perform classification in this study. The system is also shown to be fully-automated, which implies the system can be deployed widely to all places easily. To validate our study, a series of experiments have been conducted with 150 speech samples, and the score in terms of the area under the receiver operating characteristic curve is as high as 0.969, potentially outperforming the current state-of-the-art method. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79012 |
DOI: | 10.6342/NTU201803438 |
全文授權: | 有償授權 |
電子全文公開日期: | 2023-08-21 |
顯示於系所單位: | 資訊工程學系 |
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ntu-107-R05922074-1.pdf 目前未授權公開取用 | 2.1 MB | Adobe PDF |
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