Memory disorders are a central factor in the decline of functioning and daily activities in elderly individuals. The confirmation of the illness, initiation of medication to slow its progression, and the commencement of occupational therapy aimed at maintaining and rehabilitating cognitive abilities require a medical diagnosis. The early identification of symptoms of memory disorders, especially the decline in cognitive abilities, plays a significant role in ensuring the well-being of populations. Features related to speech production are known to connect with the speaker's cognitive ability and changes. The lack of standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken language. Non-lexical but acoustic properties of spoken language have proven useful when fast, cost-effective, and scalable solutions are needed for the rapid diagnosis of a disease. The work presents an approach related to feature selection, allowing for the automatic selection of the essential features required for diagnosis from the Geneva minimalistic acoustic parameter set and relative speech pauses, intended for automatic paralinguistic and clinical speech analysis. These features are refined into word histogram features, in which machine learning classifiers are trained to classify control subjects and dementia patients from the Dementia Bank's Pitt audio database. The results show that achieving a 75% average classification accuracy with only twenty-five features with the separate ADReSS 2020 competition test data and the Leave-One-Subject-Out cross-validation of the entire competition data is possible. The results rank at the top compared to international research, where the same dataset and only acoustic features have been used to diagnose patients.

中文翻译：

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通过特征选择和声学词袋模型识别口语认知衰退

记忆障碍是老年人功能和日常活动下降的核心因素。疾病的确认、开始药物治疗以减缓其进展以及开始旨在维持和恢复认知能力的职业治疗都需要医学诊断。早期识别记忆障碍症状，特别是认知能力下降，对于确保人们的福祉发挥着重要作用。众所周知，与语音产生相关的特征与说话者的认知能力和变化有关。临床环境中缺乏标准化语音测试导致人们越来越重视开发用于分析自然口语的自动机器学习技术。当需要快速、经济有效且可扩展的解决方案来快速诊断疾病时，口语的非词汇但声学特性已被证明是有用的。这项工作提出了一种与特征选择相关的方法，允许从日内瓦简约声学参数集和相对语音停顿中自动选择诊断所需的基本特征，旨在用于自动副语言和临床语音分析。这些特征被细化为单词直方图特征，其中机器学习分类器经过训练，可以对痴呆症银行皮特音频数据库中的对照对象和痴呆症患者进行分类。结果表明，利用单独的 ADReSS 2020 竞赛测试数据和整个竞赛数据的留一主题交叉验证，仅用 25 个特征即可实现 75% 的平均分类准确率。与国际研究相比，该结果排名最高，国际研究使用相同的数据集且仅使用声学特征来诊断患者。