In 2018 this project was funded by the NIHR i4i Connect programme. The project was to develop a proof of concept, which the team did working with Dr Yvonne Wren and the Bristol Speech and Language Therapy Research Unit, using the population dataset from the Avon Longitudinal Study of Parents and Children (ALSPAC).
The project won the Virgin Media VOOM Award and was listed in the HealthTech Innovation of the Year category in the Digital Leaders 100.
Approximately half of all children who have developmental language disorder (DLD) go undetected. This is partly because the NHS does have enough resources to screen all children for signs of DLD. The aim of the ATLAS project is to create an app that can screen children using machine learning. This will dramatically reduce the cost of screening and ultimately make it possible for all children to be screened.
Using speech recognition, acoustic analysis and advanced machine learning, ATLAS has automated the process of screening for developmental language disorder (DLD). The project is now entering its second phase which will include the development of a commercial product. This will save considerable time for speech language therapists and allows more children to be screened.
By introducing speech therapists to the screening process the NHS will be able to save a huge amount of resources, while also detecting more cases of developmental language disorder (DLD) at the same time.
By utilising advances in machine learning and a unique dataset, we can interpret and analyse speech from children with developmental language disorders. By directly transcribing and analysing the speech, the assessment is dramatically simplifies the screening and assessment sessions.
The ATLAS framework will offer both fixed analysis using industry standard measures as well as more in depth analysis of the language structure and content. In parallel the machine learning framework will run to either consolidate or supersede the outcome of the standard measures.