Speech-Based Real-World Scene Understanding for Assistive Care of the Visually Impaired
摘要
This research introduces a new assistive technology that combines real-time image captioning, voice synthesis, and a model-based keyword-spotting method to help people with visual impairments. In order to identify specified voice instructions as the active trigger and start a camera to record the user’s environment, the system makes use of a lightweight machine learning framework. CLIP, a cutting-edge vision-language model, is used to interpret the visual input and provide contextual textual descriptions of the surroundings. Tacotron 2, a neural text-to-speech algorithm, transforms these captions into natural-sounding speech so that users may hear their environment. The end-to-end pipeline puts usability and low latency first, showing that speech-driven activation, sophisticated picture interpretation, and high-quality audio synthesis can all be combined to provide an easy-to-use assistive tool for practical uses.