Speech Retrieval-Augmented Generation (Speech RAG) systems are increasingly used in real-world settings where spoken queries vary across speakers, emotions, and acoustic conditions. However, current evaluations primarily focus on clean-condition retrieval accuracy, leaving unclear whether these systems remain semantically invariant under paralinguistic variation. We introduce ParaSpeechRAG, a counterfactual robustness benchmark that isolates non-semantic acoustic factors while preserving lexical content. The benchmark spans controlled perturbations across speaker identity, emotion and prosody, additive noise, speaking rate, and codec compression, enabling systematic evaluation of semantic invariance in speech retrieval. We evaluate three retriever families: ASR-based cascades, speech-text multimodal retrievers (CLASP), and general-purpose audio-language models (GLAP). Results reveal a consistent robustness–accuracy trade-off: multimodal retrievers achieve higher performance under clean conditions but are substantially more sensitive to paralinguistic and acoustic perturbations, particularly emotion and noise. In contrast, ASR-based cascades are more robust to these variations due to their text-centric bottleneck, though with reduced expressive capacity. These findings highlight an overlooked dimension of instability in Speech RAG systems and underscore the need for robustness-aware evaluation beyond clean-condition benchmarks.