Development and pilot test for an AI-based mobile app to act as speech language pathology robot for long-term treatment of Cantonese-speaking children with development speech sound disorders = 開發和試點測試人工智能的手機應用程式來擔當言語語言病理學機器人來長期治療患有發展性構音障礙的粵語兒童
Project title
Development and pilot test for an AI-based mobile app to act as speech language pathology robot for long-term treatment of Cantonese-speaking children with development speech sound disorders = 開發和試點測試人工智能的手機應用程式來擔當言語語言病理學機器人來長期治療患有發展性構音障礙的粵語兒童
Principal Investigator
Department
Grant Awarding Body
Research Grants Council
Grant Type
Faculty Development Scheme
Project Code
UGC/FDS15/E01/21
Amount awarded
HK$1,412,117
Funding Year
2021-2022
Duration of the Project
30 months
Status
On-going
Abstract
Developmental speech sound disorder is a common communication disorder among young children and highly affects early childhood development and even their future educational, professional, and social success. Based on the literature review, researchers reported that at most, 24.6% of young children in the world were estimated to have speech delay or speech sound disorder (SSD). Among these children, 48.1% of 3- to 10-year-old children and 24.4% of 11- to 17-year-old children had speech sound problems only. Children with developmental SSD do not have other symptoms, and they can improve their speech intelligibility after regular training. Since Cantonese is primarily a spoken language, and written Cantonese is not acceptable in standard written Chinese, it increases the difficulty for young children in Hong Kong to learn and understand spoken Cantonese, especially for Cantonese-speaking children with SSD. Training materials for Cantonese-speaking children with SSD are much less available compared with other languages.
Once children with SSD are identified, speech-language pathologists (SLPs) select initial therapy programs for children with regular review and adjustments on therapy. An appropriate treatment program is carried out by SLPs, parents, and teachers together. The success of therapy highly relies on the effectiveness of long-term home training. There is a need for beyond-clinic measurements to support parents to accompany their children during speech therapy training at home, and to provide SLPs with better insight into the training progress of children with SSD. Furthermore, due to the insufficient number of SLPs in many places, more resources to support the work of SLPs are required; for example, helping SLPs to collect and understand the correct conditions of patients quickly during short in-clinical treatment periods. At the same, children might feel bored during long-term regular training at home and would therefore require motivation and encouragement from their parents.
To address the above problems, I propose an AI mobile app to act as a speech language pathology robot to provide training to children with SSD at home. The mobile app has a cartoon face, collects visual data from a camera and voice data from a microphone, and provides visual instructions through a screen and audio instructions through speakers. Therefore, the mobile app can act as a robot. However, parents do not need to buy the robot or learn how to use it – they simply download the mobile app, which can be accessed easily in a home setting. The mobile app can automatically monitor the training progress of children with SSD by collecting video data and voice data, and by analyzing these data in an intelligent cloud-based platform. Additionally, the analyzed results in the cloud can be used by SLPs for in-clinic consultation sessions, thus SLPs can understand the training progress of the children easily, quickly, and accurately, and provide better therapy for the children.
Once children with SSD are identified, speech-language pathologists (SLPs) select initial therapy programs for children with regular review and adjustments on therapy. An appropriate treatment program is carried out by SLPs, parents, and teachers together. The success of therapy highly relies on the effectiveness of long-term home training. There is a need for beyond-clinic measurements to support parents to accompany their children during speech therapy training at home, and to provide SLPs with better insight into the training progress of children with SSD. Furthermore, due to the insufficient number of SLPs in many places, more resources to support the work of SLPs are required; for example, helping SLPs to collect and understand the correct conditions of patients quickly during short in-clinical treatment periods. At the same, children might feel bored during long-term regular training at home and would therefore require motivation and encouragement from their parents.
To address the above problems, I propose an AI mobile app to act as a speech language pathology robot to provide training to children with SSD at home. The mobile app has a cartoon face, collects visual data from a camera and voice data from a microphone, and provides visual instructions through a screen and audio instructions through speakers. Therefore, the mobile app can act as a robot. However, parents do not need to buy the robot or learn how to use it – they simply download the mobile app, which can be accessed easily in a home setting. The mobile app can automatically monitor the training progress of children with SSD by collecting video data and voice data, and by analyzing these data in an intelligent cloud-based platform. Additionally, the analyzed results in the cloud can be used by SLPs for in-clinic consultation sessions, thus SLPs can understand the training progress of the children easily, quickly, and accurately, and provide better therapy for the children.