Embedded Implementation of Real-Time Voice Command Recognition on PIC Microcontroller
I tiakina i:
| I whakaputaina i: | Automation vol. 6, no. 4 (2025), p. 79-101 |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | , , |
| I whakaputaina: |
MDPI AG
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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MARC
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| 022 | |a 2673-4052 | ||
| 024 | 7 | |a 10.3390/automation6040079 |2 doi | |
| 035 | |a 3286259160 | ||
| 045 | 2 | |b d20251001 |b d20251231 | |
| 100 | 1 | |a Shili Mohamed |u Innov’COM Laboratory, National Engineering School of Cartahage, Ariana 2035, Tunisia; mohamed.shili@fst.utm.tn | |
| 245 | 1 | |a Embedded Implementation of Real-Time Voice Command Recognition on PIC Microcontroller | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper describes a real-time system for recognizing voice commands for resource-constrained embedded devices, specifically a PIC microcontroller. While most existing speech ordering support solutions rely on high-performance processing platforms or cloud computation, the system described here performs fully embedded low-power processing locally on the device. Sound is captured through a low-cost MEMS microphone, segmented into short audio frames, and time domain features are extracted (i.e., Zero-Crossing Rate (ZCR) and Short-Time Energy (STE)). These features were chosen for low power and computational efficiency and the ability to be processed in real time on a microcontroller. For the purposes of this experimental system, a small vocabulary of four command words (i.e., “ON”, “OFF”, “LEFT”, and “RIGHT”) were used to simulate real sound-ordering interfaces. The main contribution is demonstrated in the clever combination of low-complex, lightweight signal-processing techniques with embedded neural network inference, completing a classification cycle in real time (under 50 ms). It was demonstrated that the classification accuracy was over 90% using confusion matrices and timing analysis of the classifier’s performance across vocabularies with varying levels of complexity. This method is very applicable to IoT and portable embedded applications, offering a low-latency classification alternative to more complex and resource intensive classification architectures. | |
| 653 | |a Microcontrollers | ||
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Embedded systems | ||
| 653 | |a Classification | ||
| 653 | |a Neural networks | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Power | ||
| 653 | |a Cloud computing | ||
| 653 | |a Voice recognition | ||
| 653 | |a Signal processing | ||
| 653 | |a Power management | ||
| 653 | |a Smart houses | ||
| 653 | |a Algorithms | ||
| 653 | |a Complexity | ||
| 653 | |a Real time | ||
| 653 | |a Speech | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Hammedi Salah |u Networked Objects, Control, and Communication Systems (NOCCS), ENISo, University of Sousse, Sousse 4011, Tunisia; salah.hammedi@enim.u-monastir.tn | |
| 700 | 1 | |a Gawanmeh Amjad |u College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates | |
| 700 | 1 | |a Nouri Khaled |u Laboratory of Advanced Systems (LSA), Polytechnic School of Tunis, Al Marsa 2078, Tunisia; khaled.nouri@ept.rnu.tn | |
| 773 | 0 | |t Automation |g vol. 6, no. 4 (2025), p. 79-101 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286259160/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286259160/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286259160/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |