# Detecting Synthetic Speech Manipulation in Real Audio Recordings

arXiv: https://arxiv.org/abs/2209.07498 · HTML: https://mlsanity.com/paper/2209.07498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [SNAP: Speaker Nulling for Artifact Projection in Speech Deepfake Detection](https://mlsanity.com/paper/2603.20686/) (2026)

## Abstract (arXiv)

Recent advances in artificial speech and audio technologies have improved the abilities of deep-fake operators to falsify media and spread malicious misinformation. Anyone with limited coding skills can use freely available speech synthesis tools to create convincing simulations of influential speakers' voices with the malicious intent to distort the original message. With the latest technology, malicious operators do not have to generate an entire audio clip; instead, they can insert a partial manipulation or a segment of synthetic speech into a genuine audio recording to change the entire context and meaning of the original message. Detecting these insertions is especially challenging because partially manipulated audio can more easily avoid synthetic speech detectors than entirely fake messages can. This paper describes a potential partial synthetic speech detection system based on the x-ResNet architecture with a probabilistic linear discriminant analysis (PLDA) backend and interleaved aware score processing. Experimental results suggest that the PLDA backend results in a 25% average error reduction among partially synthesized datasets over a non-PLDA baseline.