ABSTRACT
With the numerous acoustic interferences that deteriorate speech signal quality, speech detection in loud situations is extremely difficult. The research discusses how various noise sources, including reverberation, ambient noises, and background conversation, affect the accuracy and intelligibility of speech. It also explores the shortcomings of current speech recognition systems in managing noisy environments and highlights major issues such as speaker variability, noise resilience, and real-time processing restrictions. Operating in noisy situations, where background noise, reverberation, and other acoustic interferences deteriorate the quality of voice signals, presents substantial hurdles for speech recognition systems. This study provides an extensive overview of noise-robust speech recognition methods with an emphasis on strategies and tactics to enhance system performance under challenging acoustic circumstances. A comprehensive review of current strategies and tactics for noise-robust voice recognition is given, including machine learning algorithms, feature extraction strategies, and signal processing techniques. These include, among other things, robust feature extraction strategies, noise reduction algorithms, feature enhancement techniques, and model adaption methodologies.
