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Voiced Intelligence: Bridging Speech Recognition with LLM

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  • Drashti Mehta
  • Rocky Upadhyay

Abstract

Multimodal communication systems are the result of the growing need for user-friendly and accessible human-computer interaction. The design and implementation of a voice-enabled chatbox application that enables user interaction via text and speech modes is shown in this study. In order to produce precise and contextually aware answers, the system uses automated speech recognition (ASR) to handle spoken inquiries. Natural language processing (NLP) techniques are then used to interpret the queries. The program offers flexibility and improves accessibility for users in a variety of contexts by allowing users to type inquiries in addition to using voice input. The option to export the entire chat history in PDF format is a crucial component of the system that allows users to have a permanent and portable record of their interactions. This study investigates the implementation and system architecture. The system architecture, implementation difficulties, and user experience factors are all examined in this study. The system's ability to provide smooth, multimodal communication is demonstrated by experimental evaluation, which also identifies the system's prospective uses in assistive technology, education, and customer service.

Suggested Citation

  • Drashti Mehta & Rocky Upadhyay, 2025. "Voiced Intelligence: Bridging Speech Recognition with LLM," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 12(6), pages 232-240, December.
    • Handle: RePEc:etm:ijsrst:v12:y2025:i6:id:1275
    DOI: 10.32628/IJSRST25126399
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