Title: A Comprehensive Review of AI-Driven Speech Recognition Technologies
Authors: Yewande Goodness Hassan, Bright Chibunna Ubamadu , Andrew Ifesinachi Daraojimba, Wilfred Oseremen Owobu , Olumese Anthony Abieba, Peter Gbenle
Volume: 9
Issue: 4
Pages: 113-122
Publication Date: 2025/04/28
Abstract:
As the field of artificial intelligence (AI) continues to advance, speech recognition technologies have witnessed a remarkable evolution. This comprehensive review explores the fundamental principles, AI techniques, applications, challenges, and future trends in AI-driven speech recognition. Beginning with an overview of the historical context and the transformative impact of AI on speech recognition, this paper delves into the fundamental concepts, including speech signal processing and key components of speech recognition systems. The exploration of AI techniques encompasses machine learning approaches, such as supervised and unsupervised learning, as well as deep learning techniques, including neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models. Highlighting the diverse applications of AI-driven speech recognition, the paper discusses its pivotal role in healthcare, virtual assistants, smart speakers, and customer service applications. Despite the significant strides, challenges persist, ranging from accuracy and error rates to multilingual and dialectal complexities, as well as privacy and security concerns. The review emphasizes the importance of addressing these challenges to enhance the robustness of speech recognition systems. Anticipating future trends, the paper explores advancements in neural networks, integration with other AI technologies, and the emergence of real-time and edge computing applications. A comparative analysis of leading speech recognition technologies, including Google Speech-to-Text, Amazon Transcribe, Microsoft Azure Speech, and IBM Watson Speech to Text, provides insights into their strengths and limitations. The inclusion of case studies, both successful implementations and lessons learned from failures, adds practical perspectives to the review. This paper synthesizes key findings, discusses implications for the future, and offers recommendations for further research in the dynamic realm of AI-driven speech recognition technologies.