A Multilingual Behavioral Speech Dataset for Vishing and Social Engineering Detection

Résumé

Voice phishing (vishing) has emerged as a major vector for social engineering, exploiting the emotional and persuasive aspects of speech to deceive victims. Existing public datasets are often monolingual and lack structured annotations of persuasive intent and interactional structure in telephony scenarios. VISHGUARD is a multilingual synthetic audio corpus designed to advance research on persuasion-sensitive vishing detection. It contains 3,000 simulated phone calls in English, French, and Modern Standard Arabic evenly distributed between fraudulent and legitimate categories. Each sample is annotated across several dimensions, including persuasion strategy, interactional markers, and emotional tone. The audio recordings were synthetically generated using text-to-speech synthesis and controlled noise mixing, with ambient sounds used to approximate office, street, and home-like acoustic variability rather than full telecommunication-channel effects. The dataset covers durations from 10 to 90 seconds and maintains balanced linguistic and class distributions. VISHGUARD provides a reproducible, script-conditioned synthetic telephony corpus with multidimensional annotations to support controlled research on persuasion-aware vishing detection in multilingual settings. All data files are publicly available to ensure transparency and reuse.

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