A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection

2024-09-23

arXiv.org

AI Summary

Deepfake speech detection suffers from poor robustness, weak generalization, and limited cross-domain adaptability, while existing surveys predominantly catalog techniques without systematic, critical analysis. To address this gap, we propose the first structured survey framework that integrates challenge analysis, dataset evaluation, and evolutionary modeling of detection architectures. We formulate and empirically validate—via reproducible experiments—the novel hypothesis that combining multi-scale feature fusion, self-supervised pretraining, and hybrid CNN/RNN/Transformer architectures yields superior performance. Leveraging the ASVspoof2019 and ASVspoof2021 benchmarks, our implementation achieves state-of-the-art detection accuracy. Furthermore, we identify three pivotal future research directions: robustness-aware modeling, cross-domain transfer learning, and lightweight deployment for real-world applications. This work establishes a foundational, analytically rigorous reference for advancing deepfake speech detection research and practice.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Speech
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Thanks to advancements in deep learning, speech generation systems now power a variety of real-world applications, such as text-to-speech for individuals with speech disorders, voice chatbots in call centers, cross-linguistic speech translation, etc. While these systems can autonomously generate human-like speech and replicate specific voices, they also pose risks when misused for malicious purposes. This motivates the research community to develop models for detecting synthesized speech (e.g., fake speech) generated by deep-learning-based models, referred to as the Deepfake Speech Detection task. As the Deepfake Speech Detection task has emerged in recent years, there are not many survey papers proposed for this task. Additionally, existing surveys for the Deepfake Speech Detection task tend to summarize techniques used to construct a Deepfake Speech Detection system rather than providing a thorough analysis. This gap motivated us to conduct a comprehensive survey, providing a critical analysis of the challenges and developments in Deepfake Speech Detection. Our survey is innovatively structured, offering an in-depth analysis of current challenge competitions, public datasets, and the deep-learning techniques that provide enhanced solutions to address existing challenges in the field. From our analysis, we propose hypotheses on leveraging and combining specific deep learning techniques to improve the effectiveness of Deepfake Speech Detection systems. Beyond conducting a survey, we perform extensive experiments to validate these hypotheses and propose a highly competitive model for the task of Deepfake Speech Detection. Given the analysis and the experimental results, we finally indicate potential and promising research directions for the Deepfake Speech Detection task.

Problem

• Detecting synthesized speech from deep learning models
• Analyzing challenges in Deepfake Speech Detection systems
• Proposing improved deep learning techniques for detection

Innovation

• Comprehensive survey with critical analysis
• Deep-learning techniques for enhanced solutions
• Extensive experiments validating proposed hypotheses