Dynamic Assistant for Efficient Multiple and Oral Navigation [D.A.E.M.O.N]

Madhan Kairamkonda; Suryaprakash Reddy Musku; Rahul Gogur; V. Ambica; Sony Pakkiru

doi:10.2991/978-94-6463-940-7_22

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Dynamic Assistant for Efficient Multiple and Oral Navigation [D.A.E.M.O.N]

Authors

Madhan Kairamkonda¹^{, *}, Suryaprakash Reddy Musku², Rahul Gogur³, V. Ambica⁴, Sony Pakkiru⁵

¹Student, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

²Student, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

³Student, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

⁴Assistant Professor, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

⁵Assistant Professor, Dept IT, Vignana Bharathi Institute of Technology, Hyderabad, India

^*Corresponding author. Email: kairamkondamadhan@gmail.com

Corresponding Author

Madhan Kairamkonda

Available Online 31 December 2025.

DOI: 10.2991/978-94-6463-940-7_22 How to use a DOI?
Keywords: Voice Assistant; Human-Computer Interaction; NLP; Smart Automation; Context Awareness; Edge AI
Abstract: D.A.E.M.O.N (Dynamic Assistant for Efficient Multiple and Oral Navigation) is a modular, Python-based, voice-driven assistant designed for efficient, context-aware interaction with digital systems. The framework integrates speech recognition, natural language understanding, system-level automation, and real-time information retrieval, operating entirely offline to preserve privacy and reduce latency. Evaluation demonstrates 90.37% speech recognition accuracy, 92% intent classification success, 97.1% task automation accuracy, and an average response time of 1.75 s. These results highlight D.A.E.M.O.N’s potential as a practical, low-latency solution for accessibility-focused environments, smart computing, and autonomous systems.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 31 December 2025
ISBN: 978-94-6463-940-7
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-940-7_22 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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ris enw bib

TY  - CONF
AU  - Madhan Kairamkonda
AU  - Suryaprakash Reddy Musku
AU  - Rahul Gogur
AU  - V. Ambica
AU  - Sony Pakkiru
PY  - 2025
DA  - 2025/12/31
TI  - Dynamic Assistant for Efficient Multiple and Oral Navigation [D.A.E.M.O.N]
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 300
EP  - 312
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_22
DO  - 10.2991/978-94-6463-940-7_22
ID  - Kairamkonda2025
ER  -

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