Children's Speech Disorders Identification and Therapy Treatment

PhraseFluent is a project to develop a complete web-based solution to help people who are suffering from speech disorders. This application is specifically designed for people who are suffering from articulation and stuttering speech disorders. There are few solutions available for speech disorders...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International Conference on Advancements in Computing (Online) s. 179 - 184
Hlavní autoři: Karunasekara, Pasan, Deshitha, Sheshan, De Alwis, Dinuja, Karunarathna, Desan, Lokuliyana, Shashika, Gamage, Narmada
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 07.12.2023
Témata:
Articulation disorder
Children Disorders
Ecosystems
Machine learning
Medical treatment
Mobile applications
Pediatrics
Speech Disorders
Speech therapy
Stuttering
User experience
Voice activity detection
Voice Detection
ISSN:2837-5424
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:PhraseFluent is a project to develop a complete web-based solution to help people who are suffering from speech disorders. This application is specifically designed for people who are suffering from articulation and stuttering speech disorders. There are few solutions available for speech disorders identification and speech therapies. Those available applications have several drawbacks that lead to the research gap for this research. Those findings do not have any disorder-specific speech detection methods. Those applications generally detect speech disorders not the type of the disorder. Also, the speech therapy exercises suggest applications are practicing words according to the order of the alphabet. Those solutions are not based on the sounds that need to be practiced in order to cure speech disorders. Also, applications that have detection mechanisms are not currently available for wide access. This application mainly contains React-based interactive interfaces and a Python-programmed backend with machine-learning algorithms for functionality. This application can help to automate the process of detecting speech disorders and suggest therapy activities. Also, this application can track the patient's progress with the therapies. Used datasets contain the data with 70% accuracy and the test results gave a 68% percent success rate. This approach will be the first step in automating the complete process of speech disorder diagnosis.
ISSN:2837-5424
DOI:10.1109/ICAC60630.2023.10417158