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Conference proceeding
Deepfake Speech Detection Through Emotion Recognition: A Semantic Approach
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 8962-8966
23 May 2022
Abstract
In recent years, audio and video deepfake technology has advanced relentlessly, severely impacting people's reputation and reliability. Several factors have facilitated the growing deepfake threat. On the one hand, the hyper-connected society of social and mass media enables the spread of multimedia content worldwide in real-time, facilitating the dissemination of counterfeit material. On the other hand, neural network-based techniques have made deepfakes easier to produce and difficult to detect, showing that the analysis of low-level features is no longer sufficient for the task. This situation makes it crucial to design systems that allow detecting deepfakes at both video and audio levels. In this paper, we propose a new audio spoofing detection system leveraging emotional features. The rationale behind the proposed method is that audio deepfake techniques cannot correctly synthesize natural emotional behavior. Therefore, we feed our deepfake detector with high-level features obtained from a state-of-the-art Speech Emotion Recognition (SER) system. As the used descriptors capture semantic audio information, the proposed system proves robust in cross-dataset scenarios outperforming the considered baseline on multiple datasets.
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Details
- Title
- Deepfake Speech Detection Through Emotion Recognition: A Semantic Approach
- Creators
- Emanuele Conti - Politecnico di MilanoDavide Salvi - Politecnico di MilanoClara Borrelli - Politecnico di MilanoBrian Hosler - Drexel UniversityPaolo Bestagini - Politecnico di MilanoFabio Antonacci - Politecnico di MilanoAugusto Sarti - Politecnico di MilanoMatthew C. Stamm - Drexel UniversityStefano Tubaro - Politecnico di Milano
- Publication Details
- ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 8962-8966
- Publisher
- IEEE
- Grant note
- Air Force Research Laboratory (10.13039/100006602)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000864187909055
- Scopus ID
- 2-s2.0-85131238453
- Other Identifier
- 991019173717604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Acoustics
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic