Deploying speech command recognition on multilingual edge devices demands models that are both energy-efficient and capable of cross-lingual transfer without per-language retraining—two goals rarely addressed jointly. Spiking Neural Networks (SNNs) offer substantial energy savings via sparse binary communication, yet their cross-lingual generalization remains unstudied. We present SpikeXL, a framework that investigates whether SNN spike sparsity provides an inductive bias toward language-agnostic representations. Our experiments suggest that the V_th firing threshold of Leaky Integrate-and-Fire (LIF) neurons suppresses language-specific surface phonetics while preserving command-level acoustic structure, yielding embeddings that transfer across languages more readily than dense ANN activations. Building on this finding, we introduce Spike-space Contrastive Alignment (SCA), a contrastive objective adapted for spike-rate embeddings that aligns semantically equivalent commands across languages without requiring bilingual lexicons or parallel speech data. On Speech-MASSIVE across 12 typologically diverse languages, SpikeXL achieves a Cross-Lingual Semantic Similarity (CLSS) of 0.912—outperforming a parameter-matched Transformer with the same SCA objective (0.763) and SNN without SCA (0.721)—indicating that spike sparsity itself contributes an inductive bias beyond what contrastive training alone provides. Few-shot k-NN classification on target languages (115 support samples, frozen encoder) reaches 80.8 macro-average accuracy, corroborating alignment quality with a metric fully independent of the SCA objective. On English GSC-v2, SpikeXL reaches 92.76 accuracy with 2.5M parameters and 45M synaptic operations per inference.