Speaker
Description
Speech and language impairments are common among children with autism spectrum disorder (ASD), often manifesting as difficulties with articulation, prosody, and social communication. Traditional speech and language therapy is effective but tends to follow standardized approaches that may not fully account for the unique and evolving needs of each child. This research aims to develop an AI driven platform that customizes speech and language therapy for children with ASD by analyzing individual speech patterns, prosodic features, and social communication behaviors.
The proposed platform integrates advanced machine learning models, such as natural language processing (NLP) and speech recognition, to assess and track a child's speech progress over time. By continuously monitoring features like pitch, rhythm, intonation, and volume (prosody), as well as verbal and non-verbal communication cues, the platform generates personalized therapy recommendations tailored to the child’s specific linguistic and communicative challenges. The system is designed to adapt dynamically, updating its recommendations based on ongoing analysis of real-time data collected during therapy sessions or daily interactions.
A key feature of the platform is its ability to analyze social communication behaviors, such as turn-taking, eye contact, and the use of gestures, to identify areas where additional support may be needed. The AI model learns from each interaction, adjusting its recommendations for therapy exercises, visual and auditory stimuli, and communication prompts based on the child’s progress and response patterns.
In an initial evaluation, the platform was implemented with a group of children diagnosed with ASD, yielding promising results. The system demonstrated its ability to adapt to the individual needs of each child, providing personalized feedback and tailored therapy exercises. Participants exhibited progress in various areas, such as clearer speech patterns, improved control over vocal modulation, and enhanced social interaction skills. Additionally, the platform offered therapists valuable real time insights into each child's communication behaviors, allowing for more informed and responsive intervention strategies. The system’s continuous learning and adjustment capabilities made it an effective tool for enhancing both the quality and precision of speech therapy interventions.
The research highlights the potential of AI in revolutionizing speech and language therapy for children with ASD, offering a highly personalized, scalable, and flexible solution that evolves with the child’s unique developmental needs.
