Real-Time Edge Intelligence using State-Gated Spectral VAD for Multimodal Streaming on Resource-Constrained Devices

Abstract

Multimodal AI streaming is used to enable real-time interaction in educational applications. It is a critical component in the integration of AI-driven Augmented Reality (AR) for language learning. The challenge faced in AI streaming is maintaining responsiveness on resource-constrained devices in regions with unstable network infrastructure. The previous algorithms which work well in high-bandwidth environments cannot be dominant at the network edge due to heavy resource consumption and latency spikes. This paper proposes an altered form of streaming architecture, termed State-Gated Spectral Voice Activity Detector (SG-SVAD), which produces a highly responsive, full-duplex multimodal interaction. This modified form of the streaming engine is used to generate a zero-parameter spectral Voice Activity Detection (VAD) coder which is scalable and uses the Fast Fourier Transform (FFT) harmonic ratio stages which are responsible for the acoustic and arithmetical terminations that are actually detached from heavy machine learning constraints as practically all the acoustic feedback detached during the prediction phases at the encoder side is mitigated by a state-driven audio gating mechanism. Therefore, clearly the computation phase which is modified to delegate generative loads via WebSocket telemetry produces a bit stream which is highly scalable. This modified algorithm works well at both lower memory bounds and noisy environments. Quantitative evaluations were done by measuring the Time-To-First-Audio latency and RAM resource utilization across four hardware tiers in frontier regions and the results at various conditions were noted and compared with the previous standard architectures.