Research on the application of machine learning models in speech recognition in noisy environments

Abstract

In a world increasingly reliant on verbal communication with machines, this paper undertakes a rigorous exploration of the application of machine learning models in speech recognition within noisy environments. An important focus of this research lies in demonstrating the viability and robustness of these models when challenged by real-world noise interference. In particular, the utilization of deep learning models such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) is thoroughly examined. Using concrete data and case studies, this paper uncovers a promising trajectory in the development and implementation of noise-robust speech recognition systems. In so doing, it lays a foundation for future research to enhance the practicality and effectiveness of machine learning models in this area. The ultimate goal is to ensure more reliable and accurate speech recognition that can withstand various noise conditions, thus expanding the potential for speech recognition applications in diverse and adverse environments.