Advance speech recognition / Mohammad Amarulhariz Mohd Fairuz … [et al.]

This abstract provides a thorough examination of recent advancements in the field of speech recognition, synthesizing insights from various scholarly articles and research papers. Speech recognition technology has undergone remarkable progress in recent years, driven by advancements in artificial intelligence, machine learning algorithms, and neural network architectures. This paper reviews key methodologies and approaches utilized in modern speech recognition systems, highlighting the shift from traditional statistical models to deep learning techniques. Additionally, it discusses the challenges and opportunities inherent in speech recognition, including robustness to noise, speaker variability, and multilingual support. Practical applications across diverse domains such as healthcare, telecommunications, automotive, and consumer electronics are also explored. Furthermore, the abstract outlines potential future directions for research in speech recognition, including the integration of multimodal inputs, continual learning techniques, and enhanced privacy-preserving mechanisms. Through a comprehensive analysis, this abstract offer valuable insights into the evolving landscape of speech recognition technology and its implications for academia and industry alike.