MOVES · MOdelling Vocal Expression in Schizophrenia
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2021-02-01 → 2023-01-31
- EU contribution
- €207,312
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
MOdelling Vocal Expression in Schizophrenia
Can computational and machine learning approaches to speech analysis (e.g., natural language processing (NLP), speech signal technology (SST)) be used in a diagnostic and prognostic fashion to support clinicians in the diagnosis and monitoring of neuropsychiatric condition and their clinical symptoms? Individuals with neuropsychiatric disorders present speech and language atypicalities often associated with clinical symptoms. The assessment, monitoring, and treatment of these disorders in psychiatric practice is deeply rooted in human communication: the use of SST and NLP can therefore assist clinicians in the diagnosis and monitoring of these disorders by providing them with quantitative measures of clinical features related to speech. However, our current understanding of speech and language atypicalities in neuropsychiatric conditions is very poor and is limited by the lack of a comprehensive and systematic approach, little consideration of the heterogeneity of disorders and the replicability and generalizability of previous findings. These are reasons that have so far prevented the development of effective clinical applications of these technologies in neuropsychiatric conditions. The MOVES project aimed to provide the first comprehensive account of the mechanisms underlying voice and speech atypicalities in schizophrenia, assess their impact on clinical evaluation, and lay the ground for more reliable and evidence-based screening tools. The first goal of the project was to explicitly assess how well previous speech and NLP findings generalize across a large cross-linguistic and heterogeneous dataset of voice recordings from patients with schizophrenia and controls. The second major goal of the study was to determine how various factors, i.e., sociodemographic, clinical, contextual (e.g., speech task), and cultural factors, interact in affecting speech and language production in schizophrenia. In addition, the project aimed to identify and overcome other important gaps in the current literature, such as the need for a stronger collaborative effort aimed to collect larger shared multilingual corpus representative of the heterogeneity of the schizophrenia spectrum, the need for a cumulative approach able to build on previous findings, and critical reflection on the potentialities and risks of speech-based clinical applications.
Data: CORDIS, © European Union
Project objective
The human voice is a powerful tool for social communication. In recent years, Artificial Intelligence (AI) fostered the development of advanced voice systems, able to infer considerable information from the speaker’s voice, such as emotional and mental states, mood information and personality traits. Individuals with schizophrenia (SZ) tend to present voice atypicalities, which are related to core clinical symptoms and social impairment. Recent advances in voice technology may lead the way to a revolution in the study of voice disorders. They may allow to disentangle the affective, cognitive and social mechanisms responsible for voice atypicalities, assist clinicians in diagnosis and monitoring of the disorders, and enhance their capability to capture the complex relationship between vocal behaviour, emotion regulation and clinical features. However, our present understanding of voice abnormalities in SZ is very poor, limited by the lack of comprehensive models and systematic approaches to study voice production.MOVES aims at providing a solid understanding of the implications of atypical voice patterns in SZ: through the application of machine learning and signal processing technologies (AI), I will provide a first comprehensive account of the mechanisms underlying voice atypicalities, assess their impact on clinical evaluations, and create the foundations for more reliable and evidence-based screening tools. The project aims to foster multi-centric and international collaborations to overcome important limits of this research field, such as the need for cross-linguistic studies, larger datasets, and open and collaborative research. MOVES pioneers a new area of research at the intersection between cognitive neuroscience, psychiatry, computational science and AI. An innovative aspect of the project is the intention to translate recent AI technological advances into clinical settings, to improve the way we conceptualise, assess and monitor voice disorders in SZ.
Original text from CORDIS.
Participants
- AARHUS UNIVERSITET · Aarhus CCoordinatorDenmark
Links
Data: CORDIS, © European Union