A CNN-BASED VOICE COMMAND SYSTEM FOR SLIDE CONTROL TO ENHANCE ACCESSIBILITY FOR USERS DISABILITIES - Hutagalung, Carli Apriansyah; Adi Fitrianto (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
This study presents a robust CNN-based model for real-time voice command recognition, specifically designed to recognize “right” and “left” commands. The dataset, derived from the Speech Commands Dataset, includes audio samples augmented with additional noise, yielding hundreds of thousands of data points to enhance model performance under noisy conditions. Each audio sample, approximately 1 second in length, is transformed into a spectrogram to facilitate pattern recognition by the CNN. The model was trained over 20 epochs, achieving a training accuracy of 96.5% and a validation accuracy of 97.6%, indicating strong generalization without overfitting. Testing on real-world noisy audio further demonstrated the model’s effectiveness, recording an overall accuracy of 97.7% and an AUC of 1.0 for both classes. The results underscore the model’s potential for reliable deployment in noisy environments, with low false positives and rapid response times, as indicated by CPU and memory performance metrics. These findings contribute valuable insights into designing voice-controlled systems for real-world applications, especially for users in challenging auditory environments.