Noise robust automatic speech recognition: review and analysis

Automatic Speech Recognition (ASR) system is an emerging technology used in various fields such as robotics, traffic controls, and healthcare, etc. The leading cause of ASR performance degradation is mismatch between the training and testing environments. The main reason for this mismatch is the presence of noise during the testing phase of an ASR system. Various techniques have been used by different researchers in front and backend phases of ASR, to detect and handle the noise. However, a very few review papers have considered noise as a criterion to present the comparison among the existing research works. Hence, the objective of this survey is to analyze and review all the effective methods proposed by different scientists and researchers to boost the noise robustness of an ASR system. Initially, the paper discusses the basic architecture of an ASR system, the factors affecting the its performance, and noise problem formulation. Secondly, the work analysis existing state of the art noise robust ASR methods in terms of front end feature extraction techniques and backend classification model. Then, a detailed review in terms of various speech databases, that are used by these methods, is given. Finally, an analysis in terms of performance metrics of all these noise-resistant ASR techniques is presented. Also, the paper discusses various feature extraction techniques, backend classification methods, different speech databases and performance metrics in detail, while presenting the analysis. The paper also discusses the existing challenges, and describes future research directions in the area of building noise-resistant ASR systems. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.