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'.