Automatic Speech Recognition (ASR) systems face significant challenges compared to Large Language Models (LLMs) due to the inherent complexity of accurately transcribing human speech, which involves capturing precise words, intonation, and punctuation. Unlike LLMs, which can generate language with a degree of abstraction and creativity, ASR must deliver exact ground truth without room for error, making it a more demanding task. The variability in human speech, influenced by accents, dialects, and environmental factors such as background noise and recording quality, adds layers of complexity to ASR development. Additionally, ASR systems contend with data scarcity, as collecting diverse and high-quality voice datasets raises ethical concerns and is more challenging than obtaining text data. Despite these hurdles, voice interfaces offer unique opportunities for personalization and engagement, making ASR a critical area of AI development with the potential to create deeply human-centric applications. As technology advances, ASR is expected to play an increasingly important role in how we interact with digital environments, promising a future where understanding people through voice becomes a cornerstone of AI innovation.