Command Recognition System Using Convolutional Neural Networks

This paper addresses the problem of ASR (Automatic Speech Recognition), a crucial field for the advancement of HCI (Human-Computer Interaction) technologies and applications in voice assistants and machine translation. The proposed methodology employs CNNs (Convolutional Neural Networks) to predict spoken commands from voice signals by extracting essential features such as Spectrograms, ZCR (Zero Crossing Rate), Spectral Centroid, Spectral Rolloff, and MFCCs (Mel-Frequency Cepstral Coefficients). A filter bank was also implemented to enhance signal processing. To evaluate the system’s performance, comparisons of accuracy across different processing methods were conducted. Additionally, a hardware implementation using the Simulink graphical programming environment and a Raspberry Pi microcontroller was developed, demonstrating the system’s practicality and efficiency in real-world contexts. The results indicate that CNNs, coupled with these specific features, provide a better accuracy in speech recognition compared to traditional methods. The implementation on the Raspberry Pi confirms the potential for deploying this system in practical applications, paving the way for the development of cost-effective and efficient ASR solutions.