Leveraging Emotion Recognition to Power Adaptability for More Effective Speech Therapies

Speech therapy is a medical area focused on diagnosing and treating speech impairments, which affect an individual’s ability to communicate effectively and develop linguistic skills. While these difficulties can arise at any stage of life, they are most commonly observed in childhood. In this context, technology plays a crucial role in supporting therapists while also enhancing patient engagement, reducing boredom, and minimizing frustration during treatment. To address this challenge, the article explores the integration of AI-driven emotion recognition techniques in a web platform called e-SpeechT that supports the actors involved in speech therapy (i.e., therapist, caregiver and patient) when creating, managing and performing it. This research work proposes new functionalities that can be implemented to improve the effectiveness of the treatment while making the system more adaptable to patients’ needs, skills and emotional states, fostering a seamless human-AI symbiosis. The main objective of e-SpeechT is to ensure a more sustainable usage and development of resources while providing an easier access to the treatment.