arXiv:2509.22808v2 Announce Type: replace Abstract: With the rise of generative text-to-speech models, distinguishing between real and synthetic speech has become challenging, especially for Arabic that have received limited research attention. Most spoof detection efforts have focused on English, leaving a significant gap for Arabic and its many dialects. In this work, we introduce the first multi-dialect Arabic spoofed speech dataset. To evaluate the difficulty of the synthesized audio from each model and determine which produces the most challenging samples, we aimed to guide the constructi
The rapid advancement of generative text-to-speech models has made distinguishing synthetic from real speech increasingly difficult, necessitating new detection benchmarks.
This work addresses a significant gap in Arabic speech spoofing detection, which has implications for cybersecurity, information integrity, and the development of robust AI systems in non-English linguistic contexts.
The introduction of the first multi-dialect Arabic spoofed speech dataset will enable more effective research and development in combating audio deepfakes in a crucial and previously underserved language.
- · Cybersecurity firms
- · AI researchers in Arabic NLP
- · Governments/organizations combating misinformation
- · Malicious actors using Arabic speech deepfakes
- · Traditional audio forensics
Improved detection capabilities for Arabic deepfake audio could reduce the impact of misinformation campaigns.
This could lead to a 'deepfake arms race' where spoofing technology and detection methods rapidly evolve in tandem.
Enhanced trust in audio communications and media, particularly in regions where Arabic is prevalent, is a long-term potential outcome.
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