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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62258
Volume:
10 |
4 |
April 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
Department of Electronics & Computer Science
Dr. A. D. Shinde College of Engineering, Bhadgaon,Gadhinglaj
This paper presents a Voice-Controlled AI Assistant that enables users to interact with a computer using simple voice commands. The system captures speech through a microphone, converts it into text using speech recognition, and processes it with natural language techniques to understand user intent. Based on the command, it performs tasks such as opening applications, searching information, and executing basic operations. The assistant is developed using Python and relevant libraries, ensuring flexibility and ease of use. The system demonstrates reliable performance with quick responses under normal conditions, making human-computer interaction more efficient and user-friendly while reducing dependence on traditional input devices.
KEYWORDS
The project has created an AI personal assistant robot powered by a voice command interface that operates with the use of the Raspberry Pi 4 Model B. In addition, the ability to respond to voice commands, along with the ability to move forwards or backwards, left or right, and to stop, is a key feature of this device. The inclusion of facial detection technology adds a basic ability for the robot to recognize faces.
[1] L. R. Rabiner and B. H. Juang, Fundamentals of Speech Recognition. Prentice Hall, 1993. doi: 10.1109/5.18626. [2] D. Jurafsky and J. H. Martin, Speech and Language Processing, 2nd ed. Pearson, 2009. doi: 10.1017/CBO9780511809078. [3] T. N. Sainath and C. Parada, “Convolutional neural networks for small-footprint keyword spotting,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, pp. 1478–1482. doi: 10.1109/ICASSP.2015.7178195. [4] A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013, pp. 6645–6649. doi: 10.1109/ICASSP.2013.6638947.
Publication Date
5/2/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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