Samvad-DF: Unmasking Partial Audio Deepfakes in Multispeaker Conversational Settings

Audio deepfake detection is entering a new regime. Rather than synthesizing entire utterances, emerging attacks manipulate authentic recordings by seamlessly replacing a sentence or even a single word. While visual deepfake research has progressively expanded from isolated face swaps to complex multi-person scenarios, audio detection has largely remained focused on single-speaker English speech and binary utterance-level classification, with limited emphasis on localizing manipulated content. To address this gap, we introduce SAMVAD-DF, the first large-scale benchmark for partial audio deepfakes in multispeaker Hindi conversations. Built from 107 hours of in-the-wild debates and podcasts, the dataset captures substantial acoustic diversity, containing recordings with up to 76 speakers. SAMVAD-DF defines three granularities of manipulation, ranging from speaker-level manipulations to highly challenging sub-second word-level replacements. Constructing this benchmark also reveals a fundamental limitation of conventional training. Existing pipelines assign file-level labels to every short audio window, causing clean regions far from the splice to be treated as manipulated during supervision. We instead introduce a validated, strictly bounded temporal annotation protocol that labels only the true manipulated span, enabling faithful localization learning. Experiments demonstrate a clear trade-off between detection and localization. Training with precise temporal labels improves boundary-overlap localization by approximately one-third, while increasing conventional file-level error by around 10 percentage points. These findings highlight that strong utterance-level performance does not necessarily imply accurate localization. SAMVAD-DF provides both evaluation protocols and diagnostic tools to facilitate rigorous research on the next generation of audio deepfake detection.