Building automated speech recognition has become one of the most resource-intensive arms races in modern computing. For years, the industry standard has centered on scaling foundational transformer models across hundreds of thousands of hours of speech data to drive Word Error Rates closer to zero. Yet, as speech-to-text engines have become ubiquitously integrated into virtual meeting software, streaming platforms, and classroom tools, a fundamental limitation has become increasingly obvious to anyone relying on them for daily communication. While modern speech models are remarkably adept at transcribing vocabulary, they remain largely oblivious to the emotional tone, cadence, and urgency that give spoken language its actual meaning. In high-stakes technical environments—such as engineering sprint retrospectives, architectural design reviews, or advanced university STEM lectures—spoken dialogue is rarely delivered as flat prose. A slight upward inflection can turn an apparent statement of fact into a skeptical question; a sudden drop in vocal pitch can signal a serious warning about a code vulnerability; and an urgent delivery can differentiate a critical design constraint from a casual suggestion. When standard automated speech recognition strips these acoustic cues away, leaving behind an uninflected block of text at the bottom of a display, it creates what Human-Computer Interaction (HCI) researchers call an intention gap. For deaf and hard-of-hearing professionals, the result is continuous cognitive strain, forcing them to guess speaker intent while rapidly darting their visual attention between slides, physical demonstrations, and disconnected caption windows. At the University of California, Irvine and Virginia Tech, HCI researcher Dr. Sunday David Ubur has taken an alternative engineering path to address this challenge. Rather than treating speech recognition solely as a sequence-to-sequence text translation problem, Dr. Ubur approaches the challenge as an asynchronous distributed systems and spatial computing problem. Across several years of published laboratory work, his research has centered on