The Intelligent Speech Fluency and Sign Translation System is designed to enhance communication accessibility and speech development through advanced artificial intelligence techniques. The system integrates speech recognition, natural language processing, and computer vision to analyze spoken language and translate it into corresponding sign language gestures in real time. It also evaluates speech fluency by detecting pronunciation errors, pauses, repetitions, articulation issues, and speech rate irregularities. A deep learning–based acoustic model processes audio signals, while a convolutional neural network interprets sign gestures for bidirectional communication. The proposed framework supports inclusive interaction between hearing-impaired individuals and fluent speakers, promoting social integration and educational support. The system architecture ensures low latency and high accuracy through optimized preprocessing, feature extraction, and classification modules. Experimental evaluation demonstrates improved fluency assessment accuracy and reliable sign translation performance under diverse environmental conditions. This solution can be deployed in educational institutions, healthcare centers, and public service environments to foster inclusive and intelligent communication.
Madanachitran et al. (Thu,) studied this question.