Comprehensive Insights into Noise Mitigation for Automatic Speech Recognition Systems

Abstract

Introduction: It is a comprehensive analysis of the advancements in Automatic Speech Recognition (ASR) systems in the presence of environmental noise, focusing on the challenges posed by various noise types and the evolution of noise mitigation strategies. Environmental noise significantly degrades ASR performance, leading to increased Word Error Rate (WER). The study categorizes background noise available during acoustic production sources for different kinds of surroundings and emphasizes the need for robust noise identification to enhance ASR efficiency. Various mitigation strategies including deep learning techniques, noise reduction algorithms, and model adaptations are explored, along with their effectiveness in real-time applications. This review adheres to the PRISMA guidelines to synthesize literature from peer-reviewed journals, identifying key methodologies adopted for noise recognition and suppression from 2010 to the present. Additionally, it outlines the transition from traditional feature-based methods to modern deep learning approaches such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which facilitate improved noise classification and enhance speech intelligibility in challenging environments. The review highlights ongoing challenges and future research directions aimed at optimizing ASR systems for diverse applications in socio-technological contexts.