HIR SDD HIR SDD (Human annotated Inference Reasoning for Spoofing and Deepfake Detection) is a dataset of speech clips with human written natural language reasoning and structured spoofing cue tags. Each row links an audio clip to an explanation of why the clip was judged genuine or spoofed, together with a discrete set of perceptual reason tags. Paper: Towards Robust Speech Deepfake Detection via Human Inspired Reasoning Dataset: marsianin500/HIR SDD Raw annotations: marsianin500/HIR SDD raw Dataset summary Item Value Annotation rows 88,630 Unique audio clips 45,036 Crowd annotation rows 84,678 SpeechLLM derived annotation rows 3,952 Task Given a speech clip, models may be trained or evaluated to: 1. classify the clip as genuine ( bonafide ) or spoofed ( fake ); 2. predict structured reason tags; 3. generate or evaluate natural language reasoning about the decision. Annotation format Each row is one annotation for one clip. The same audio id may appear in multiple rows when several annotators labeled the same clip. Field Description audio id Stable clip identifier path Relative path to bundled audio in this repository source corpus Upstream audio corpus source Annotation origin: m…
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