Integrating Deep Learning for Real-Time Speech Recognition in Noisy Environments
Enhancing Autonomous System Decision-Making
Keywords:
Deep Learning, Noisy Environments, Convolutional Neural Networks, Transformers, Noise Suppression, Signal ProcessingAbstract
The integration of deep learning algorithms in real-time speech recognition has significantly advanced the capability to process and understand speech in noisy environments. These environments, such as crowded public spaces and industrial settings, pose considerable challenges for traditional speech recognition systems, which often struggle to filter out background noise. This paper explores various deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models, emphasizing their effectiveness in enhancing speech recognition accuracy in the presence of noise. Additionally, the paper discusses the methodologies employed to improve signal quality, such as noise suppression techniques and data augmentation. The implications of these advancements for various applications, including automated customer service, industrial monitoring, and accessibility tools, are also examined. By identifying the challenges faced in developing robust speech recognition systems for noisy environments, this paper highlights future directions for research and potential solutions to enhance performance.
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