Department of Electronics & Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India.
Department of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru 560035, India.
Sensors (Basel). 2022 Nov 29;22(23):9311. doi: 10.3390/s22239311.
Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a clear indication of dementia and plays a crucial role in the recognition process. Even though earlier studies have used speech features to recognize subjects suffering from dementia, they are often used along with other linguistic features obtained from transcriptions. This study explores significant standalone speech features to recognize dementia. The primary contribution of this work is to identify a compact set of speech features that aid in the dementia recognition process. The secondary contribution is to leverage machine learning (ML) and deep learning (DL) models for the recognition task. Speech samples from the Pitt corpus in Dementia Bank are utilized for the present study. The critical speech feature set of prosodic, voice quality and cepstral features has been proposed for the task. The experimental results demonstrate the superiority of machine learning (87.6 percent) over deep learning (85 percent) models for recognizing Dementia using the compact speech feature combination, along with lower time and memory consumption. The results obtained using the proposed approach are promising compared with the existing works on dementia recognition using speech.
痴呆症会影响患者的记忆，并导致语言障碍。研究表明，言语和语言恶化通常是痴呆症的明显迹象，并在识别过程中起着关键作用。尽管早期的研究已经使用语音特征来识别患有痴呆症的患者，但这些特征通常与从转录中获得的其他语言特征一起使用。本研究探讨了用于识别痴呆症的重要独立语音特征。这项工作的主要贡献是确定一组有助于痴呆症识别过程的精简语音特征。次要贡献是利用机器学习 (ML) 和深度学习 (DL) 模型进行识别任务。本研究使用痴呆症银行中的 Pitt 语料库中的语音样本。为该任务提出了韵律、语音质量和倒谱特征的关键语音特征集。实验结果表明，对于使用精简语音特征组合识别痴呆症，机器学习（87.6%）模型优于深度学习（85%）模型，同时还降低了时间和内存消耗。与使用语音识别痴呆症的现有工作相比，使用所提出方法获得的结果很有前景。
