Citra Digital Voice Recognition Menggunakan SVD

Voice recognition is a crucial area in digital signal processing and artificial intelligence. In this research, we propose an innovative method for voice recognition using Singular Value Decomposition (SVD) on digital images. This approach aims to enhance the accuracy and efficiency of voice recognition by leveraging the image representation of sound generated through the SVD process. We integrate image processing techniques with machine learning-based voice recognition models to create a system capable of accurately identifying and distinguishing sound patterns. The proposed method is tested using various sound datasets covering a range of variations and conditions, and the experimental results demonstrate a significant improvement in voice recognition accuracy compared to conventional methods. Therefore, this approach shows promise as a significant contribution to the development of reliable and efficient voice recognition systems. Keywords: Voice recognition, Digital images, Singular Value Decomposition (SVD), Machine learning, Voice recognition accuracy