Speech Sound Disorders (SSDs) encompass a range of difficulties in speech planning and production that affects as many as 20% of preschool children. A SSD not only puts a child at serious and immediate educational disadvantages, activity limitations, frustration, and isolation but also has a huge impact on a child's quality of life. Timely and accurate diagnosis is therefore vital to receiving the right treatment, and reducing the impact of the disorder. However, due to several significant barriers, it is difficult to determine the presence or absence of a SSD and to differentially diagnose a SSD. These barriers are insufficient time and clinical resources, limited clinical expertise in remote areas, and inefficient and variable assessment procedures with compromised accuracy.
We have a SMAAT solution!
The Speech Movement and Acoustic Analysis Tracking (SMAAT) team is working to develop software for automatic speech movement and acoustic analysis. The software will use artificial intelligence and machine learning to support clinical diagnosis of a SSD.
Our SMAAT software innovation will be a portable non-invasive web or stand-alone application that will revolutionise the health services through improving the accuracy and (time) efficiency of differential diagnosis of SSDs in children. The availability of the SMAAT could allow allied health professionals to determine the urgency of care (triage) for patients and could help alleviate the strain on the healthcare system. This would reduce waiting time for access to a speech-language pathologist, introduce a telehealth option and reduce the need for face-to-face contact, particularly beneficial for rural and remote populations. This platform will increase the accuracy of diagnosis, reduce administration time, lower financial cost, and provide timely access to personalised intervention.
A multi-disciplinary and highly skilled project team comprising of national and international experts in the area of speech science, computer vision, artificial intelligence and clinical speech-language pathology is collaborating to create the SMAAT innovation.
The overall objective of this project is to use machine learning to create and evaluate an easy-to-use and relatively low cost software platform for the differential diagnosis of SSDs. The platform will utilise a customised word list to derive scores from acoustic, articulatory movement (jaw, lips, tongue) and clinical data inputs.
Our research aims are as follows:
Speech-language pathologists (S-LP) play a primary role in the assessment, diagnosis and treatment of children with speech sound disorders. Currently, the assessment process for diagnosis and differential diagnosis of speech sound disorder subtypes requires time consuming administration of several tests (i.e. a test battery approach). This typically includes a case history, oral examination, speech sound assessment (accuracy of child's production of speech sounds) and speech motor testing (precision of speech movements of jaw, lips, tongue etc). These assessments are scored subjectively by listening to child's speech and/or by looking at the child's speech movements, using a pen and paper based approached.
Increasingly, speech science researchers are encouraging S-LPs to integrate objective measures into the assessment process. However, S-LPs face many barriers to achieving this in the clinical setting that include: cost, time, specialist expertise and suitability of the equipment for use with young children. Additionally, expertise to extract and interpret the data is required. These all present as large barriers to S-LPs obtaining objective measures and therefore restrict the capacity to provide an accurate and timely differential diagnosis.
CI Helmholz is a Senior Lecturer at Curtin University with specialist expertise in Photogrammetry. She has an interdisciplinary research track record, with a strong connection to clinical research. She has 54 publications, an h-index of 7 in Scopus and of 9 in Google and attracted $1,573,890 cash awarded research grants. Petra has supervised 8 PhD students (3 have graduated), 4 Master by research students (1 has graduated), 6 Master by course work students (all graduated), 15 Bachelor with Honour students (14 have graduated), 16 internship students and 57 undergraduate projects (54 have graduated). She has access to national and international networks. CIB Helmholz is chair of the Surveying and Spatial Sciences Institute’s (SSSI) Remote Sensing and Photogrammetry (RSP) committee; co-chair of a working group of the International Society of Remote Sensing and Photogrammetry (ISPRS); and associate editor of a large international Photogrammetry journal (Photogrammetric Engineering and Remote Sensing). She has won five awards with the highlights being the 2016 Curtin University Commercial Innovation Awards for Deep Water 3D imaging, the 2017 Asia Pacific Spatial Excellent Award (Education Development), and the 2019 Western Australian Spatial Excellent Award –Spatial Enablement (Industry Award) for the project Cliniface.