Optimizing Deep Neural Networks for Speech Recognition Systems

Abstract

Deep neural networks (DNNs) have revolutionized the field of speech recognition, offering significant improvements in accuracy and efficiency. However, optimizing these networks for real-world applications, such as virtual assistants, transcription systems, and voice-activated devices, remains a challenge. This article explores various techniques and strategies for optimizing deep neural networks to improve their performance in speech recognition tasks. The study discusses the role of network architecture, data augmentation, regularization, and transfer learning in enhancing model efficiency. Additionally, it highlights the challenges faced in deploying DNNs for speech recognition, including computational complexity, memory constraints, and real-time performance requirements.