Preprocessing and Capability–Risk Coupling in an Event-Synchronized Dual-Frontend Speech Sensor System

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An intelligent speech sensor is often evaluated at recognition output, although acquisition and preprocessing also determine what reaches control. We examine this propagation in an event-synchronized dual-frontend system. A conditional cascade separates artifact availability, evidence formation, semantic conversion, and executor persistence. It decomposes how scheduled intent survives to the action boundary from raw audio through controlled motion. Paired recordings compare an INMP441–ESP32 interface with a USB interface. In an exploratory study, five speakers each delivered 30 Chinese utterances in three acoustic conditions, giving 450 scheduled events, 1800 ASR paths, and 3600 decision paths. Twelve missing artifacts remained in the intention-to-test denominators. Under the frozen peak normalization rule, isolated full-scale INMP samples limited window gain, and normalized USB–INMP speech-band differences reached +20.96 to +29.52 dB. Mean cloud text availability was 48.0 percentage points higher on USB. Exact valid execution rose by 41.7 points for DeepSeek and by 26.7 points for MiMo. Unauthorized non-stop motion on reject events rose by 19.6 points for DeepSeek and by 13.0 points for MiMo. Capability and risk increased together in all ten cloud speaker–consumer contrasts. Among 564 dangerous reject paths, 340 contained no literal motion token. In the worst case per speaker, hold-last replay produced 72.2–100% dangerous reject states. This is an exploratory sensor system case study of the tested frontend and preprocessing configuration, not a capsule ranking. Preprocessing changed useful evidence reach and action opportunity, so coverage, authorization, and acknowledged executor state need joint evaluation. The empirical results are specific to the tested pipeline and the five observed speakers.