Can LLMs Help Localize Fake Words in Partially Fake Speech?

Abstract

Large language models (LLMs), trained on large-scale text, have recently attracted significant attention for their strong performance across many tasks. Motivated by this, we investigate whether a text-trained LLM can help localize fake words in partially fake speech, where only specific words within a speech are edited. We build a speech LLM to perform fake word localization via next token prediction. Experiments and analyses on AV-Deepfake1M and PartialEdit indicates that the model frequently leverages editing-style pattern learned from the training data, particularly word-level polarity substitutions for those two databases we discussed, as cues for localizing fake words. Although such particular patterns provide useful information in an in-domain scenario, how to avoid over-reliance on such particular pattern and improve generalization to unseen editing styles remains an open question.

arXiv commentSubmitted to Interspeech 2026; put on arxiv based on requirement from Interspeech: "Interspeech no longer enforces an anonymity period for submissions." and "For authors that prefer to upload their paper online, a note indicating that the paper was submitted for review to Interspeech should be included in the posting."