C.C. Gerber, J.R. Gerber
ShadowGen Inc,
United States
Keywords: AI-native communications, edge AI systems, resilient communications, low-bandwidth networking, degraded networks, data-centric architectures, distributed AI systems
Summary:
Voice communication is often the first capability to fail in environments constrained by low bandwidth, intermittent connectivity, or heterogeneous network infrastructure. Conventional voice systems rely on waveform-based audio codecs that assume continuity in packet delivery and timing. When these assumptions break, intelligibility degrades rapidly, imposing structural limits on voice reliability under degraded conditions. This work presents ShadowGen, an AI-native voice communications architecture that treats spoken voice as structured data rather than as a continuous audio waveform. Instead of transmitting compressed audio, speech is represented as compact AI-derived voice representations optimized for intelligibility and conversational continuity rather than audio fidelity. This approach decouples voice intelligibility from link quality and enables functional speech communication at data rates significantly below those required by conventional waveform-centric systems. The contribution of this work is architectural rather than algorithmic. The system explicitly separates voice representation, transport, and reconstruction into distinct layers, allowing each to be optimized independently. This separation enables voice traffic to be routed, buffered, and multiplexed as structured data across heterogeneous and disrupted network environments without reliance on waveform continuity or stable transport conditions. The architecture is designed for deployment on constrained mobile platforms and does not assume specialized infrastructure or high-capacity links. Early proof-of-concept prototypes on mobile devices demonstrate intelligible voice communication under simulated degraded network conditions at sub-kilobit operating regimes. In this context, intelligibility is defined functionally as the ability to support correct conversational understanding and task-level coordination, rather than high-fidelity audio reproduction. The current work emphasizes architectural feasibility and system behavior under constraint, rather than exhaustive performance optimization or benchmarking. While initially motivated by operational environments where network reliability cannot be assumed, including emergency response and remote connectivity scenarios, the underlying architecture applies broadly to any setting where voice continuity and correctness are prioritized over audio quality. This work establishes AI-native voice transport as a viable foundation for next-generation resilient communications systems operating under constrained and heterogeneous network conditions.