Jarvis-Virtual Voice Assistant

Abstract

Although voice recognition technology has advanced significantly, there are still obstacles in the way of obtaining high accuracy, especially in a variety of environmental settings. This research uses deep learning models to increase the robustness and accuracy of voice recognition systems. Implementing different deep learning architectures, such as recurrent and convolutional neural networks (RNNs), and training them on a wide range of datasets with different noise levels and accents was part of the technique. Important results show a 15% improvement in recognition accuracy over current systems for the suggested models, which perform especially well in noisy contexts and with accented speech. These outcomes demonstrate the effectiveness of deep learning techniques in resolving issues that traditional voice recognition systems encounter. The results of this study have important significance for real-world applications, since they may enable smooth communication in a variety of linguistic and environmental contexts. Context: Since voice recognition technology has come a long way, it still has problems with accuracy, particularly in noisy situations and different locations with different accents. Even with advancements, traditional systems find it difficult to function consistently and dependably under these circumstances. This lays the groundwork for the need to investigate more durable ways to raise voice recognition's accuracy and dependability