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Speech-to-Text Command Recognition for IoT Home Automation Using Convolutional Neural Networks

Author

Listed:
  • V Pavan Kalyan
  • S Rahil
  • Shaik Suhaib
  • K Venkata Bharath
  • Shaik Zaidan
  • Ajay Sharma

Abstract

The rapid proliferation of the Internet of Things (IoT) has revolutionized smart home environments; however, traditional interfaces such as tactile switches or mobile applications remain inaccessible to physically challenged or elderly demographics. To address this accessibility gap, this paper proposes a robust, limited-vocabulary speech recognition system designed for edge computing environments. The system utilizes Mel-frequency cepstral coefficients (MFCC) for feature extraction and a Convolutional Neural Network (CNN) for keyword spotting. Optimized for low-resource hardware, the model is deployed on a Raspberry Pi using TensorFlow Lite to control home appliances via voice commands. Experimental results demonstrate that the proposed system achieves high classification accuracy with low latency, providing a viable, hands-free solution for home automation without relying on continuous cloud connectivity.

Suggested Citation

  • V Pavan Kalyan & S Rahil & Shaik Suhaib & K Venkata Bharath & Shaik Zaidan & Ajay Sharma, 2026. "Speech-to-Text Command Recognition for IoT Home Automation Using Convolutional Neural Networks," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 12(3), pages 689-696, June.
    • Handle: RePEc:jbh:ijsrcs:v12:y2026:i3:id:2074
    DOI: 10.32628/CSEIT26123367
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26123367
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