{"type":"ScholarlyArticle","title":"Speech-Forensics: Towards Comprehensive Synthetic Speech Dataset Establishment and Analysis","authors":[{"name":"Zhoulin Ji","slug":"zhoulin-ji","given":"Zhoulin","family":"Ji","affiliation":"School of Software Engineering , Xi'an Jiaotong University"},{"name":"Chenhao Lin","slug":"chenhao-lin","given":"Chenhao","family":"Lin","affiliation":"School of Cyber Science and Engineering , Xi'an Jiaotong University"},{"name":"Hang Wang","slug":"hang-wang","given":"Hang","family":"Wang","affiliation":"School of Automation Science and Engineering , Xi'an Jiaotong University; Department of Computing , The Hong Kong Polytechnic University"},{"name":"Chao Shen","slug":"chao-shen","given":"Chao","family":"Shen","affiliation":"School of Cyber Science and Engineering , Xi'an Jiaotong University"}],"abstract":"Detecting synthetic from real speech is increasingly crucial due to the risks of misinformation and identity impersonation. While various datasets for synthetic speech analysis have been developed, they often focus on specific areas, limiting their utility for comprehensive research. To fill this gap, we propose the Speech-Forensics dataset by extensively covering authentic, synthetic, and partially forged speech samples that include multiple segments synthesized by different high-quality algorithms. Moreover, we propose a TEmporal Speech LocalizaTion network, called TEST, aiming at simultaneously performing authenticity detection, multiple fake segments localization, and synthesis algorithms recognition, without any complex post-processing. TEST effectively integrates LSTM and Transformer to extract more powerful temporal speech representations and utilizes dense prediction on multi-scale pyramid features to estimate the synthetic spans. Our model achieves an average mAP of 83.55% and an EER of 5.25% at the utterance level. At the segment level, it attains an EER of 1.07% and a 92.19% F1 score. These results highlight the model's robust capability for a comprehensive analysis of synthetic speech, offering a promising avenue for future research and practical applications in this field.","doi":"10.24963/ijcai.2024/46","url":"https://ijcai.org/proceedings/2024/46","pdf":"https://ijcai.org/proceedings/2024/0046.pdf","markdown":"https://ijcai.org/proceedings/2024/46.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/46","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI Ethics, Trust, Fairness","track":"Main Track","number":46,"pages":{"first":413,"last":421},"keywords":["Machine Learning: ML: Deep learning architectures","Natural Language Processing: NLP: Speech","AI Ethics, Trust, Fairness: ETF: Safety and robustness","Machine Learning: ML: Structured prediction"],"videos":[],"references":[{"position":1,"raw":"[Alegre et al., 2012] Federico Alegre, Ravichander Vipperla, and Nicholas Evans. 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