ProSDD: Learning Prosodic Representations for Speech Deepfake Detection against Expressive and Emotional Attacks
Abstract
Speech deepfake detection (SDD) systems perform well on standard benchmarks datasets but often fail to generalize to expressive and emotional spoofing attacks. Many methods rely on spoof-heavy training data, learning dataset-specific artifacts rather than transferable cues of natural speech. In contrast, humans internalize variability in real speech and detect fakes as deviations from it. We introduce ProSDD, a two-stage framework that enriches model embeddings through supervised masked prediction of speaker-conditioned prosodic variation based on pitch, voice activity, and energy. Stage I learns prosodic variability from real speech, and Stage II jointly optimizes this objective with spoof classification. ProSDD consistently outperforms baselines under both ASVspoof 2019 and 2024 training, reducing ASVspoof 2024 EER from 25.43% to 16.14% (2019-trained) and from 39.62% to 7.38% (2024-trained), while achieving 50% relative reductions on EmoFake and EmoSpoof-TTS.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of ProSDD, a novel framework that enhances speech deepfake detection by incorporating prosodic features through a two-stage learning process, significantly improving performance against expressive and emotional spoofing attacks. This work represents a meaningful advancement in the field of audio processing and deepfake detection, addressing critical challenges in generalization and robustness.
Methodology
The proposed ProSDD framework introduces a two-stage approach that effectively integrates prosodic features into the speech deepfake detection process. By first training on real speech to learn speaker-conditioned prosodic variations, the model is better equipped to generalize to emotional and expressive attacks. The use of supervised masked prediction as an auxiliary task during spoof classification is innovative and highlights the importance of prosody in distinguishing between genuine and synthetic speech. The methodology is well-structured, with clear delineation of the training stages, and the use of a lightweight classifier emphasizes the contribution of the prosodic representations rather than complex architectures.
Results & evaluation
The experiments conducted on standard benchmarks (ASVspoof 2019 and 2024) and emotional datasets (EmoFake and EmoSpoof-TTS) demonstrate significant improvements in performance, particularly in reducing the Equal Error Rate (EER) for expressive attacks. The results are compelling, showing that ProSDD outperforms existing baselines, which indicates a strong experimental design. The paper provides sufficient detail on datasets, implementation, and evaluation metrics, allowing for a thorough understanding of the experimental setup.
Reproducibility
The authors have committed to releasing the ProSDD framework publicly, which supports reproducibility. The paper includes detailed implementation specifics, such as training epochs, batch sizes, and hyperparameter settings, which are crucial for others to replicate the results. However, the absence of a direct link to the code repository limits the ease of access for potential users.
Limitations
While the framework shows promise, it may still be sensitive to the diversity of the training data used in Stage I. If the real speech data does not adequately represent the variability in natural speech, the model's generalization capabilities could be compromised. Additionally, the reliance on a lightweight classifier may limit performance in more complex scenarios where deeper architectures could provide benefits.
Broader impact
The implications of this research are significant, particularly in the context of combating the growing issue of speech deepfakes, which can have serious consequences in misinformation and identity theft. By improving detection methods, this work contributes to the development of more robust systems that can be applied in various domains, including security, media verification, and user authentication. The main contribution of this paper is the introduction of ProSDD, a novel framework that enhances speech deepfake detection by incorporating prosodic features through a two-stage learning process, significantly improving performance against expressive and emotional spoofing attacks. This work represents a meaningful advancement in the field of audio processing and deepfake detection, addressing critical challenges in generalization and robustness.
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