We present an approach to automatic detection of Alzheimer's type dementia based on characteristics of spontaneous spoken language dialogue consisting of interviews recorded in natural settings. The proposed method employs additive logistic regression (a machine learning boosting method) on content-free features extracted from dialogical interaction to build a predictive model. The model training data consisted of 21 dialogues between patients with Alzheimer's and interviewers, and 17 dialogues between patients with other health conditions and interviewers. Features analysed included speech rate, turn-taking patterns and other speech parameters. Despite relying solely on content-free features, our method obtains overall accuracy of 86.5 , a result comparable to those of state-of-the-art methods that employ more complex lexical, syntactic and semantic features. While further investigation is needed, the fact that we were able to obtain promising results using only features that can be easily extracted from spontaneous dialogues suggests the possibility of designing non-invasive and low-cost mental health monitoring tools for use at scale.
@cite_24: To help individuals with Alzheimer’s disease live at home for longer, we are developing a mobile robotic platform, called ED, intended to be used as a personal caregiver to help with the performance of activities of daily living. In a series of experiments, we study speech-based interactions between each of 10 older adults with Alzheimers disease and ED as the former makes tea in a simulated home environment. Analysis reveals that speech recognition remains a challenge for this recording environment, with word-level accuracies between 5.8 and 19.2 during household tasks with individuals with Alzheimer’s disease. This work provides a baseline assessment for the types of technical and communicative challenges that will need to be overcome in human-robot interaction for this population.
@cite_27: Mental health (including substance abuse) is the fifth greatest contributor to the global burden of disease, with an economic cost estimated to be US $2.5 trillion in 2010, and expected to double by 2030. Developing information systems to support and strengthen population-level mental health monitoring forms a core part of the World Health Organization's Comprehensive Action Plan 2013–2020. In this paper, we review recent work that utilizes social media ‘big data’ in conjunction with associated technologies like natural language processing and machine learning to address pressing problems in population-level mental health surveillance and research, focusing both on technological advances and core ethical challenges.
From the perspective of potential applications of automatic speech analysis to technology-assisted care, there is evidence @cite_24 that it is psychologically more acceptable for a user to be aided by another person or a robot than from ambient sensors and devices which are unable to offer meaningful interaction. Therefore, the development of such assistive applications involves research on speech processing for natural conversations rather than scripted speech or monologues @cite_27 .