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.

The Detection of stress from speech signal is gaining large attention recently. The emergence of new methods and techniques for feature extraction and classification paved the way to different solutions to detect different stress conditions using human speech and led to an in-crease in the accuracy of stress recognition. A large number of parameters are proposed for the characterization of stress in speech. Similarly numerous classifiers and machine learning algorithms are investigated for stress classification and regression. In this treatise, a recital on the commonly used databases, stress conditions, different feature extraction methods and classifiers along with some of the statistical measures as well as compensation techniques for stress detection are presented in this article. After thorough illustration of existing methodology for the task, future prospects for the work are elaborated.