Papers by Farhan Sheth
Bridging Attribution and Open-Set Detection using Graph-Augmented Instance Learning in Synthetic Speech (2026.eacl-long)
Copied to clipboard
| Challenge: | Synthetic speech detection is a critical part of safeguarding digital communication, enabling systems to identify and mitigate the risks posed by highly realistic, machine-generated voices. |
| Approach: | They propose a framework that combines SFMs with graph-based modeling and open-set generalization to capture meaningful relationships between utterances and recognize speech that doesn’t belong to any known generator. |
| Outcome: | The proposed framework improves performance across both tasks, with Mamba-based embeddings delivering particularly strong results. |