Doctoral Training Partnerships: a range of postgraduate training is funded by the Research Councils. For information on current funding routes, see the common terminology at https://www.ukri.org/apply-for-funding/how-we-fund-studentships/. Training grants may be to one organisation or to a consortia of research organisations. This portal will show the lead organisation only.

Motor speech disorders (MSDs) are speech disorders with neurological cause that affect the planning, control or execution of speech (Duffy 2019). A dysarthria is a type of MSD that reflect abnormalities in the movement required for speech production (Duffy 2019). Some common neurological causes of dysarthria are Parkinson's Disease, Multiple Sclerosis, and Cerebral Palsy. Furthermore, the psychosocial impacts (e.g. to identity, self-esteem, and social participation & quality of life) of dysarthria are well documented for individuals with dysarthria, and their family and carers (Walshe & Miller 2011). Speech technologies have a fundamental role in the clinical management of atypical speech, and the proceeding impact on an individual's quality of life. Automatic speech recognition (ASR) (i.e. the task of transforming audio data to text transcriptions) has important implications for assistive communication devices and home environment systems. Alternative and Augmentative Communication (AAC) is defined as a range of techniques that support or replace spoken communication. The Royal College of Speech and Language Therapists (RCSLT) outline the use of AAC devices in the treatment of individuals with MSDs (RCSLT 2006), and AAC devices have become standard practice in intervention. Although the accuracy of ASR systems for typical speech have improved significantly (Yue et al. 2022), there are challenges that have limited dysarthric ASR system development and limit the generalisation of typical speech ASR systems to dysarthric speech, namely: 1) high variability across speakers with dysarthria, and high variability within a dysarthric speaker's speech, and 2) limited availability of dysarthric data. Accordingly, studies have focused on i) adapting ASR models trained on typical speech data to address the challenge of applying typical speech models to dysarthric speech and ii) collecting further dysarthric data (although the volume and range of dysarthric data remains limited) (Yue et al. 2022). Furthermore, the classification of dysarthria, including measures of speech intelligibility are important metrics for the clinical (and social) management of dysarthria, including assessment of the severity of dysarthria and functional communication (Guerevich & Scamihorn 2017). The RCSLT promotes individually-tailored goals in context of the nature and type of dysarthria, underlying pathology and specific communication needs (RCSLT 2006). In current 1 practice, metrics are based on subjective listening evaluation by expert human listeners (RCSLT 2006) which require high human effort and cost Janbakhshi et al. (2020). Recent studies have implemented automated methods to classify dysarthric speech, including automatic estimators of speech intelligibility (Janbakhshi et al. 2020). To advance the application of speech technologies to the clinical management of atypical speech, the current project aims to 1) collect a corpus of dysarthric data to increase the volume of quality dysarthric data available to the research community, 2) improve the performance of dysarthric ASR systems, including investigation of methods of adapting ASR models trained on typical speech, and 3) create automated estimators for the classification of dysarthria. References Guerevich, N. & Scamihorn, L. (2017), 'SLP use of intelligiblity measures in adults with dysarthria', American Journal of SLP pp. 873-892. Janbakhshi, P., Kodrasi, I. & Bourlard, H. (2020), 'Automatic pathological speech intelligibility assessment exploiting subspace-based analyses', IEEE, 1717-1728. RCSLT (2006), Communicating Quality 3, Oxon: RCSLT. Walshe, M. & Miller, N. (2011), 'Living with acquired dysarthria: the speaker's perspective', Disability and Rehabilitation 33(3), 195-203. Yue, Z., Loweimi, E., Christensen, H., Barker, J. & Cvetkovic, Z. (2022), 'Dysarthric speech recognition from raw waveform with parametric cnns'.

There are large differences in life expectancy and the burden of ill health across the UK, both geographically and between different socioeconomic groups. In order to address health inequalities resources must be directed towards policies and programmes that not only improve health overall, but that also reduce the health gap. One of the difficulties for decision makers, however, is that they often do not know what impact a proposed programme will have on health inequalities, or indeed on overall health. This is a particular problem when the proposed programme is complex, an example being the current School Travel Plan initiative. This particular programme has many potential effects on health, both positive and negative, and these may differ in different areas and in different populations. One approach to predicting the costs and health effects of a health promoting programme is through health economic modelling. These mathematical models attempt to replicate the real world in a simplified fashion, combining evidence from diverse sources in order to estimate the outcome of a proposed course of action. This project aims to develop such a model for the school travel plan programme in order to estimate costs, health consequences and the impact on health inequality.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.