Investigating speech technology for monitoring disease progression in the context of neurodegenerative disease

A life expectancy beyond 60 years old provides us with unprecedented life opportunities. However, it also increases our risk of suffering from age-related health conditions that would prevent us from enjoying such opportunities. Dementia is one of the main such conditions. The most frequent cause of dementia is Alzheimer’s Disease (AD), which is known to start silently up to 20 years before observable symptoms appear. Therefore, interdisciplinary research efforts are focusing on unravelling disease progression and developing strategies for secondary prevention (i.e. where disease is present, but no symptoms have emerged). Whilst memory loss is usually regarded as the most prominent of these symptoms, language decline is also an essential source of clinical information that is gradually receiving more attention, given language ubiquity. The development of artificial intelligence (AI), particularly speech analysis, natural language processing and machine learning, offers opportunities for the automatic analysis of spoken language data. The work presented in this thesis applies an AI approach to the context of dementia, addressing three interrelated research questions, [1] is automatic speech analysis a viable approach to detecting pre-clinical stages of dementia? [2] if so, what type of language (i.e. monologue vs. dialogue, conversational vs. narrative, spontaneous vs. scripted) is most suitable for such analysis? and [3] which characteristics of spoken language are most predictive and practically useful (i.e. linguistic vs. acoustic)? In order to answer these questions, a systematic review of the last twenty years of literature is conducted to assess the state-of-the-art. The results of such a review indicate that this is a very promising emerging field, although research outcomes are still far from being translated into clinical practice and medical research. Subsequently, a computational analysis is performed in order to compare older adults with and without dementia on the basis of features extracted from their spoken language. The two main datasets which are available in the field are used. One of them is a monologue, task-based corpus: the Pitt Corpus; and the other one is a dialogue, conversation-based corpus: the Carolina Conversations Collection (CCC). Acoustic features are extracted from both datasets and used as input for machine learning classification, achieving a classification accuracy of up to 78.70% for Pitt Corpus, and up to 85.21% for CCC. The extracted acoustic features consist of a comprehensive set of prosodic parameters. Finally, the Prevent-ED project is presented, which was specifically designed in the context of this thesis in order to address some of the limitations gathered from the review and experiments. Prevent-ED is a protocol for a novel study design, where spontaneous language is recorded by prompting natural and rich conversations over the map of an imaginary land. Different to previous research, which involved already diagnosed cohorts, this project involves middle-age healthy adults at different levels of risk of developing dementia later in life. Accordingly, the main objective is to investigate whether speech features are predictive of such levels of risk, instead of diagnostic labels. Therefore, the design, development, implementation and dissemination of the Prevent-ED project constitute a major contribution of this doctoral research. Moreover, new data has been collected through this project and preliminary analyses show a promising tendency that is likely to be strengthened when the data collection is complete, currently on hold due to COVID-19. In conclusion, the contribution of this doctoral research is three-fold: an extensive systematic review, the first one of the field; an analysis of the two main available datasets containing speech produced by individuals with and without dementia; and Prevent-ED, a novel protocol for data collection and analysis, which attempts to offer further insights into the “at risk” stages of the disease, different to previous research on already diagnosed cohorts. The resulting high-quality database will be distributed to the wider research community