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Speech-Based Real-World Scene Understanding for Assistive Care of the Visually Impaired

Author

Listed:
  • Tarun Sunil

    (Amrita Vishwa Vidyapeetham)

  • K. Vinod

    (Amrita Vishwa Vidyapeetham)

  • M. Madhav

    (Amrita Vishwa Vidyapeetham)

  • Joshua Abraham

    (Amrita Vishwa Vidyapeetham)

  • G. Jyothish Lal

    (Amrita Vishwa Vidyapeetham)

Abstract

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.

Suggested Citation

  • Tarun Sunil & K. Vinod & M. Madhav & Joshua Abraham & G. Jyothish Lal, 2025. "Speech-Based Real-World Scene Understanding for Assistive Care of the Visually Impaired," Springer Series in Reliability Engineering,, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-98728-1_2
    DOI: 10.1007/978-3-031-98728-1_2
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