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The Fake-or-Real (FoR) dataset is a collection of more than 195,000 utterances from real humans and computer generated speech. The dataset can be used to train classifiers to detect synthetic speech. The dataset aggregates data from the latest TTS solutions (such as Deep Voice 3 and Google Wavenet TTS) as well as a variety of real human speech, including the Arctic Dataset...
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We present the MLAAD dataset, which is a multi-language dataset for the task of audio anti-spoofing. This dataset has been created using a diverse set of text-to-speech (TTS) models, and is designed to evaluate the out-of-domain generalization of anti-spoofing systems, both with respect to new languages, as well as new TTS models. Specifically, MLAAD comprises: 678.3 hours of synthetic...
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CodecFake: Enhancing Anti-Spoofing Models Against Deepfake Audios from Codec-Based Speech Synthesis Systems TL;DR: We show that better detection of deepfake speech from codec-based TTS systems can be achieved by training models on speech re-synthesized with neural audio codecs. This dataset is released for this purpose. See our paper and Github for more details on using our...
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The In-the-Wild dataset contains real and synthetic speech recordings of 58 celebrities and politicians, collected from online videos. It provides a realistic benchmark for testing how well audio deepfake detection models generalize beyond laboratory data such as ASVspoof. Task: Audio Classification (Deepfake / Genuine) Languages: English Modality: Audio Size: 37.9 hours total 17.2 hours fake 20.7 hours real
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SpeechFake is a large-scale multilingual dataset for speech deepfake detection, featuring over 3 million fake samples across 46 languages. Generated using 30 diverse open-source models* spanning text-to-speech (TTS), voice conversion or clone (VC), and neural vocoder (NV) methods, it offers rich metadata and strong coverage of modern generation techniques, enabling robust and generalizable detection research.
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This is the Zenodo repository for the ASVspoof 5 database. ASVspoof 5 is the fifth edition in a series of challenges which promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previous challenges, the ASVspoof~5 database is built from crowdsourced data collected from around 2,000 speakers in diverse acoustic conditions. More than 20...
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All existing databases of spoofed speech contain attack data that is spoofed in its entirety. In practice, it is entirely plausible that successful attacks can be mounted with utterances that are only partially spoofed. By definition, partially-spoofed utterances contain a mix of both spoofed and bona fide segments, which will likely degrade the performance of countermeasures trained with...
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The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and deepfakes and the development of countermeasures.
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- deepfake
- synthetic speech (8)
- audio data (6)
- spoof (3)
- text-to-speech (TTS) (3)
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