Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals
This addresses the challenge of identifying spliced audio in multimedia forensics, though it is incremental as it builds on existing detection methods by focusing on artifacts.
The paper tackles the problem of detecting partially fake speech signals by analyzing splicing artifacts introduced during signal concatenation, achieving detection EERs of 6.16% on PartialSpoof and 7.36% on HAD datasets without training a detector.
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.