AI-Based Stuttering Therapy and Improvement System
Yedumati Iswarya,
Yarlagadda Yashmika,
Yandrapragada Poornima Thayaru,
Yalavarthi Gnana Saraswathi,
Yadlapalli Navitha,
Dr. Akula Suneetha
Stuttering affects several dimensions of human life, including speech quality, self-perception, academic
participation, and social interaction. Typical symptoms include sound repetitions, prolongations, and
silent blocks, all of which can increase stress and make everyday communication difficult. Although
speech therapy is an effective intervention, it often involves high cost, limited access to trained specialists,
and difficulty in sustaining guided self-practice.
To address these limitations, this paper presents an AI-based stuttering therapy and improvement
system that uses artificial intelligence and machine learning to analyze speech patterns in real time.
The proposed system evaluates pronunciation, fluency, speech rate, pauses, and repetitions using speech
recognition and intelligent analysis techniques. It then provides real-time feedback, personalized therapy
recommendations, and structured daily practice sessions for both children and adults. By improving
accessibility, affordability, and consistency of practice, the system aims to help users communicate more
fluently and confidently.
