AI Enhancing Communication: Sign to Text and Voice Recognition for Hearing and Speech Impaired

This paper discusses the advances that artificial intelligence has done to better augment communication among deaf, hard of hearing, and speech-impaired communities. Sign language is converted into text using sophisticated computer vision techniques powered by deep learning architectures that recognize as well as decode sign language gestures through intelligent recognition frameworks in real-time. The system thus converts hand movements, facial expression, and body posture into synthesized speech or text that creates interaction between deaf, hard-of- hearing, and their non-signing counterparts. The AI-enabled voice recognition systems run natural language processing and models to interpret different speech patterns, such as those associated with dysarthria, stuttering, or aphasia, into intelligible forms of speech or text. These amaze with prioritization of seamless communication with education, employment, a public platform thereby making accessibility and inclusivity a priority. Key improvements include adaptive feedback mechanisms that update recognition accuracy through continual user interaction and interface customizations based on top models by employing machine learning, thus maintaining real-time with minimal latency to ensure natural and smooth interaction. However, certain challenges remain: high accuracy across different signed languages and regional dialects, variations in spoken patterns, data privacy, and affordable technologies. Ongoing academic research is directed at training datasets providing cross- linguality, federated learning against the backdrop of privacy- preserving AI models, and low-cost hardware implementations to address the problems the hearing or speech disability community faces in getting empowering tools for independence and social acceptance.