Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals

TL;DR

This paper investigates artifacts caused by splicing in partially fake speech signals, revealing detection methods that do not require training and highlighting challenges in creating undetectable spliced audio.

Contribution

It provides an analysis of splicing artifacts in fake speech, demonstrating effective detection without training and discussing implications for future research.

Findings

Detection EER of 6.16% on PartialSpoof dataset

Detection EER of 7.36% on HAD dataset

Splicing artifacts can be exploited for detection without training

Abstract

Speech deepfake detection has recently gained significant attention within the multimedia forensics community. Related issues have also been explored, such as the identification of partially fake signals, i.e., tracks that include both real and fake speech segments. However, generating high-quality spliced audio is not as straightforward as it may appear. Spliced signals are typically created through basic signal concatenation. This process could introduce noticeable artifacts that can make the generated data easier to detect. We analyze spliced audio tracks resulting from signal concatenation, investigate their artifacts and assess whether such artifacts introduce any bias in existing datasets. Our findings reveal that by analyzing splicing artifacts, we can achieve a detection EER of 6.16% and 7.36% on PartialSpoof and HAD datasets, respectively, without needing to train any detector. These results underscore the complexities of generating reliable spliced audio data and lead to discussions that can help improve future research in this area.