ºù«Ӱҵ

Assistive Technologies for Cultural Arts-Based Engagement 
Continuing to engage with (or trying out new) arts and cultural activities is associated with a better quality of life for people living with dementia and their carers. Collaborating with researchers across music, psychology and engineering, we are investigating how we can harness emerging technologies to boost opportunities for older adults living with dementia and their carers to interact with music, whether it be listening to music, creating playlists, singing, songwriting, or playing a musical instrument. We are currently conducting a scoping review that examines existing assistive technologies for the wider creative arts, as well as engaging with creative arts organisations and public and patient involvement groups (PPI) in an effort to develop and design new tools. Projected outcomes are a set of prototypes for engaging with music, as well as generic guidelines for development that can be applied across the creative arts.

Contact : Dr Jennifer MacRitchie

Automatic speech recognition for atypical voices
ºù«Ӱҵ has a long-standard track record in this area. We were the first to introduce more mainstream techniques like deep learning to improve performance (e.g., Christensen et al, 2012; Christensen et al, 2013a; Christensen et al, 2013b). In the homeService project) we implemented an online system that we deployed in people's houses long-term. This was the demonstration system for an EPSRC programme grant. The system was one of the first cloud-based systems and we were able to demonstrate state-of-the-art performance for the users. We have subsequently made the collected database public: (e.g., Green et al, 2016; Nicolao et al, 2016; Malavasi et al, 2016). We extended this work (Google Research Award; (DeepArt)) by exploring the use of deep-learning to obtain articulatory representations (Xiong et al, 2018, 2019, 2020). Currently, an ESR on the H2020 ITN-ETN TAPAS works on improving continuous dysarthric speech recognition (Yue et al, 2020a, 2020b). Related, PhD student Lubna Alhinti is working on the automatic recognition and detection of linguistic as well as paralinguistic information in speech (Alhinti et al, 2018, 2020a, 2020b) Funded by EU, Google, MRC (Confidence in Concept) Round 3.

Contact : Dr Heidi Christensen

Detection of verbal and non-verbal traits in speech and language
As part of a multi-disciplinary team involving neuroscientists, neuropsychologists, a clinical linguist and a general practitioner, we have led the technical work on developing a stratification test for people with memory concerns where a virtual agent asks memory-probing questions and the underlying speech analysis and machine learning looks for signs of neurodegenerative dementia in a person's speech and language (Mirheidari et al, 2016; 2017a; 2017b; 2018, 2019, 2020). Currently, Yilin Panan ESR on TAPAS is working with Philips on home-based monitoring for dementia (Pan *et al* 2019, 2020a, 2020b). In 2019 Dr Dan Blackburn and Dr Heidi Christensen received the Rosetrees Trust Interdisciplinary Prize for AI and medicine to port this technology to work for assessing cognitive health for stroke survivors, COMPASS. In 2019, we took the CognoSpeak system to Kenya to work with a local neurologist (GRCF pump priming funds) to begin work on exploring how to make the underpinning speech analytics more language-agnostic. Recently, we have started working with Megan Thomas, one of the CDT students on a PhD collaboration with Apple on speech and language-based automatic tracking of depression and anxiety. Related, PhD student Attas is developing a system for automatically analysing psychotherapy-client sessions with the aim of detecting clues for potential rupture. Wider applications of the use of conversational agents in therapy include a motivational system for people with COPS that was co-created and had an initial evaluation of a prototype system (Easton et al 2020), and the exploration of a similar system for creating an empathy AI agent for online peer-mediated mental health  intervention (Easton et al, in preparation). Funded by EU, John Hopkins workshop grant, MRC (Confidence in Concept) Round 6, Rosetrees Trust, CLARHC and SHSC RCF NIHR.

Contact : Dr Heidi Christensen