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arXiv AI By Sidi Chang, Peiying Zhu

Generation Provenance Before Behavior Attribution: Auditing Synthetic Speech Research Objects

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The paper proposes a generation‑provenance substrate for synthetic speech research objects, binding source specifications, generated content, waveform, target, fact requirements, quality signals, review lineage, and an immutable manifest identity. It audits this substrate in a private Japanese care‑handoff pipeline, documenting 113 assets and 1.552 hours of synthetic speech with linked audio, transcripts, notes, and fact checklists, while noting selective human evidence and source‑specific gaps. The authors argue that provenance is necessary but not sufficient for behavior attribution, requiring additional frozen training runs and intervention evidence, and they provide a compact provenance contract, audit protocol, and a bounded case study. "whyItMatters":"The study highlights the need for detailed provenance records to enable reliable auditing and attribution of synthetic data behavior, underscoring limitations in current practices and offering a structured framework for future research."

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