Bridging communication gap for hearing impaired Sb

We suggest an AI-based Sign Language Translation Platform that bridges the gap in communication among hearing-impaired and hearing people by providing real-time ISL recognition and speech/text conversion. The solution uses computer vision, NLP, and speech synthesis to convert ISL gestures into text or speech and vice versa.Key Features. Real-Time ISL to Speech/Text Translation:Utilizes computer vision and custom models trained using Roboflow to recognize gestures and translate them into natural language. 2. Speech to Sign Translation (Reverse Mode):Translates voice input into animated sign equivalents for two-way interaction. 3. Offline & Edge Processing:Facilitates accessibility in remote and low-connectivity areas with TensorFlow Lite models. 4. Emotion Detection:Employs facial expression detection for empathetic, human-like interaction. 5. Analytics Dashboard:Delivers data insights for policymakers, schools, and NGOs to ensure inclusivity.PROPOSED SOLUTION 3. ROLE OF GEN AI IN THE SOLUTION Gesture Data Augmentation: Synthetic generation of variant ISL gesture datasets by GANs or diffusion models can enhance accuracy and accommodate regional variations. Speech & Text Generation: Contextual speech or text generation through generative models (e.g., OpenAI Whisper, GPT-based models) produces natural-sounding conversation flow. Emotion-aware Responses: AI adjusts tone, style, and language based on user emotion and context. Multilingual Translation: Generative transformers (Hugging Face / Google Translate APIs) dynamically translate ISL into various Indian languages for increased inclusivity.IBM Data Prep Kit – For efficient data preprocessing, cleaning, and augmentation before training gesture and speech models. IBM Granite Models (Agentic AI) – For building advanced, context-aware conversational agents capable of reasoning and adapting dynamically to user interactions. IBM RAG (Retrieval-Augmented Generation) – To enhance the AI’s accuracy .