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Paralinguistic and linguistic fluency features for Alzheimer's disease detection
Computer Speech & Language ( IF 3.1 ) Pub Date : 2021-02-10 , DOI: 10.1016/j.csl.2021.101198
Edward L. Campbell , Raúl Yañez Mesía , Laura Docío-Fernández , Carmen García-Mateo

Alzheimer’s disease (AD) is one of the most common forms of dementia in the world. The Mini-Mental State Examination, a tool developed to detect AD, is composed of various tests that evaluate functional performance in several fields, one of which is language. Several symptoms are manifested in voices as a result of language and speech problems caused by AD, including frequent involuntary pauses during conversations and diction and vocabulary difficulties. Speech fluency is considered a key feature for AD detection in this research, for which two algorithms are proposed. The first algorithm is a paralinguistic system that is independent of the language and task and whose low-dimension feature vectors facilitate the training stage. This algorithm is tested on two databases (AcceXible and ADReSS), on two languages (Spanish and English) and on several tests. The second algorithm is based on analysing temporal patterns of silence between words and errors in spoken words. This approach, based on verbal fluency tests, is tested on the AcceXible database. To benchmark these algorithms, two baseline algorithms are used: the i-vector framework, a speaker modelling algorithm that has been effectively used for speech-related tasks such as speaker recognition, language identification, speaker diarization and speech-related health tasks; and a classic counting-terms algorithm, which processes transcriptions of speech. The paralinguistic system yields promising results for different tests and languages, while the silence-based system achieves high accuracy in verbal fluency tests.



中文翻译:

用于阿尔茨海默氏病检测的语言和语言流利性功能

阿尔茨海默氏病(AD)是世界上最常见的痴呆形式之一。迷你精神状态考试是一种用于检测AD的工具,由各种测试组成,这些测试评估了多个领域的功能性能,其中之一是语言。AD引起的语言和语音问题会在语音中表现出几种症状,包括对话中频繁的非自愿停顿,听写和词汇困难。语音流利度被认为是本研究中AD检测的关键特征,为此提出了两种算法。第一种算法是一种独立于语言和任务的副语言系统,其低维特征向量有助于训练阶段。该算法已在两个数据库(AcceXible和ADReSS)上进行了测试,两种语言(西班牙语和英语)和几种测试。第二种算法基于分析单词之间的沉默和口头单词错误的时间模式。此方法基于口语流利性测试,已在AcceXible数据库上进行了测试。为了对这些算法进行基准测试,使用了两种基准算法:i-vector框架,一种说话人建模算法,已经有效地用于与语音有关的任务,例如说话人识别,语言识别,说话人区分和与语音有关的健康任务;和经典的计数项算法,用于处理语音转录。副语言系统针对不同的测试和语言产生了可喜的结果,而基于沉默的系统则在口语流利性测试中实现了很高的准确性。第二种算法基于分析单词之间的沉默和口头单词错误的时间模式。此方法基于口语流利性测试,已在AcceXible数据库上进行了测试。为了对这些算法进行基准测试,使用了两种基准算法:i-vector框架,一种说话人建模算法,已经有效地用于与语音有关的任务,例如说话人识别,语言识别,说话人区分和与语音有关的健康任务;和经典的计数项算法,用于处理语音转录。副语言系统针对不同的测试和语言产生了可喜的结果,而基于沉默的系统则在口语流利性测试中实现了很高的准确性。第二种算法基于分析单词之间的沉默和口头单词错误的时间模式。此方法基于口语流利性测试,已在AcceXible数据库上进行了测试。为了对这些算法进行基准测试,使用了两种基准算法:i-vector框架,一种说话人建模算法,已经有效地用于与语音有关的任务,例如说话人识别,语言识别,说话人区分和与语音有关的健康任务;和经典的计数项算法,用于处理语音转录。副语言系统针对不同的测试和语言产生了可喜的结果,而基于沉默的系统则在口语流利性测试中实现了很高的准确性。为了对这些算法进行基准测试,使用了两种基准算法:i-vector框架,一种说话人建模算法,已经有效地用于与语音有关的任务,例如说话人识别,语言识别,说话人区分和与语音有关的健康任务;和经典的计数项算法,用于处理语音转录。副语言系统针对不同的测试和语言产生了可喜的结果,而基于沉默的系统则在口语流利性测试中实现了很高的准确性。为了对这些算法进行基准测试,使用了两种基准算法:i-vector框架,一种说话人建模算法,已经有效地用于与语音有关的任务,例如说话人识别,语言识别,说话人区分和与语音有关的健康任务;和经典的计数项算法,用于处理语音转录。副语言系统针对不同的测试和语言产生了可喜的结果,而基于沉默的系统则在口语流利性测试中实现了很高的准确性。处理语音转录。副语言系统针对不同的测试和语言产生了可喜的结果,而基于沉默的系统则在口语流利性测试中实现了很高的准确性。处理语音转录。副语言系统针对不同的测试和语言产生了可喜的结果,而基于沉默的系统则在口语流利性测试中实现了很高的准确性。

更新日期:2021-02-12
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