Child–robot interaction places real demands on perception systems: a robot engaged in play with a child cannot afford to miss the child’s focus of attention, yet running every perception module at every frame is computationally expensive on the kind of edge hardware that privacy-respecting robots must use. This paper addresses this tension by proposing a cadence-stratified multimodal perception pipeline, WordMate-CRI, in which each module runs at a rate that trades computation against staleness, informed by how long each cue remains stable for a potential interaction partner. Detection and tracking run continuously; action recognition uses rolling temporal buffers; emotion is refreshed periodically; and attributes such as age and gender are estimated once at the start of a session and cached. We evaluated WordMate-CRI through an LLM response-quality ablation study across six toy-play scenarios and a latency benchmark on Jetson AGX Orin hardware. The ablation shows that VLM enrichment and object evidence are the dominant grounding signals, with WordMate-CRI achieving a mean scene-grounded score of 0.967. On live Jetson input, cadence-based scheduling raises throughput from 4.3 to 18.2 frames per second (FPS), clearing the 15 FPS deployment target, while reducing WordMate-CRI latency from 6411.5 to 5844.5 ms. Taken together, the results make a practical case that deciding when each perception module should run matters just as much as deciding what it should recognise. This optimisation is important for systems aiming to produce contingent and timely child-directed speech in multi-modal contexts.