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.

The detection of audio deepfakes (ADD) has become increasingly important due to the rapid evolution of generative speech models. However, progress in this field remains uneven across languages, particularly for low-resource languages like Portuguese, which lack high-quality datasets. In this paper, we introduce BRSpeech-DF, the first publicly available ADD dataset for Portuguese, encompassing both Brazilian and European variants. The dataset contains over 458,000 utterances, including a smaller portion of real speech from 62 speakers and a large collection of synthetic samples generated using multiple zero-shot text-to-speech (TTS) models, each conditioned on the original speaker’s voice. By providing this resource, our objective is to support the development of robust, multilingual detection systems, thereby advancing equity in speech forensics and security research. BRSpeech-DF addresses a significant gap in annotated data for underrepresented languages, facilitating more inclusive and generalizable advancements in synthetic speech detection.