Advancements in Speech Recognition: A Comprehensive Survey of Machine Learning Techniques with a Focus on GAN-AE Integration

Abstract

The utilization of speech recognition technology assumes a critical role in contemporary applications, encompassing virtual assistants and transcription services. This thorough evaluation paper examines the present state of voice recognition with a specific emphasis on machine learning methodologies, particularly the integration of Generative Adversarial Networks and Autoencoders (GAN-AE). The paper presents a comprehensive analysis of methodologies including supervised and unsupervised learning as well as deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Deep Neural Networks (DNNs). It accentuates the obstacles encountered within the field such as the robustness to noise, scarcity of data and real-time processing while proposing innovative solutions that leverage GAN-AE.  The significance of GAN-AE is emphasized through practical case studies that showcase its efficacy in various applications ranging from augmenting voice assistants to adapting to different accents and limited data scenarios. The findings underscore the potential of this technology to enhance accuracy and adaptability across diverse domains. Additionally, this document recognizes potential avenues for future exploration, proposing an investigation of the incorporation of voice recognition with other modalities as well as addressing ethical considerations regarding prejudice and privacy. In summary, this survey paper contributes to the ongoing discourse on speech recognition by providing valuable insights, novel solutions, and directions for future research, all in the pursuit of more precise and responsible technology.