The MOVES project followed the following workflow:
1) First, we systematically reviewed and meta-analyzed the evidence for distinctive speech and language patterns in schizophrenia. The meta-analyses (MA) indicated a plausible speech and language profile associated with schizophrenia, i.e. reduced speech production, pitch variability, and semantic coherence. However, the MA also identified some critical issues, such as: i) small sample size of previous studies ii) limited attention to the heterogeneity of the disorder iii) limited attempts at theory-driven research that directly addresses the mechanisms underlying atypical language patterns iv) no cross-linguistic studies assessing the role of language and cultural diversity.
2) We thus collected a large cross-linguistic dataset of audio recordings of patients with schizophrenia (n=437) and controls (n=445) in four different languages (Danish, German, Mandarin-Chinese, Japanese). This corpus allowed us to: (a) systematically assess the generalizability of results from MA across different languages and samples for the first time in schizophrenia, thus better accounting for cross-linguistic differences2,3; b) provide a comprehensive assessment of patients' symptomatology and clinical profile by modeling the relationships between acoustic features and pharmacotherapy, relevant clinical aspects, demographic and social features; c) test the cross-linguistic generalizability of voice-based machine learning models (ML) for predicting the diagnosis of schizophrenia.
3) Overall, we found little evidence for a universal speech and language profile characterizing schizophrenia. Speech and language patterns were highly heterogeneous, with different sources of heterogeneity interacting at different levels. We found that only two results from the previous MA could be generalized across all the different languages of our corpus (lower pitch variability and lower sentence-level coherence), whereas most speech and language features showed different patterns across languages or were inconsistent with results from MA. Cross-linguistic generalization of voice-based ML models for predicting schizophrenia was generally low too, with performance close to change when a model was trained on a language (e.g. Danish) and tested on a different language (e.g. Chinese). These results raise some questions about the generalizability of previous findings and the possibility of building on them cumulatively. However, they also point to where future attempts might be directed: larger shared multilingual corpora representative of the heterogeneity of the schizophrenia spectrum, a more systematic assessment of the generalizability of results under more varied conditions to account for heterogeneity of disorders, more collaborative work and multi-sites studies.
4) To address this issue, the project contributed to establish and foster an international collaborative network to study the mechanisms underlying speech and language markers in schizophrenia. This network included several partners, such as the DISCOURSE consortium (
https://discourseinpsychosis.org/(opens in new window)) and it is now contributing to: a) promote large-scale world-wide cross-linguistic data collection c) homogenize the speech data collection procedure across sites c) create an open data repository d) seek concrete opportunities for clinical trials application and research funding.
5) The project produced 7 scientific papers published in international journals and 7 conference abstracts. The work carried during the project has been disseminated in many different ways, including social networks, conferences, workshops and fairs. A special topic in Frontiers in Psychology has been published, and a workshop titled “Voice- and speech-based markers of neuropsychiatric conditions: assessing methodological foundations for clinical application” has been held at the host institution.
