WildSpoof challenge

WildSpoof Image

WildSpoof

The WildSpoof Challenge aims to advance the use of in-the-wild data in two speech processing tasks that generates and detects spoofed speech. We invite you to participate in the WildSpoof Challenge, designed to advance the use of in-the-wild data in two critical and increasingly intertwined speech processing tasks:

  1. Text-to-Speech Generation (TTS)
  2. Spoofing-aware Automatic Speaker Verification (SASV)

The WildSpoof Challenge promotes research that bridges the gap between speech generation and spoofing detection, fostering interdisciplinary innovation towards more robust, realistic, and integrated speech systems. Specifically, we set the following objectives:

  1. Advance the use of in-the-wild data in two closely related but underexplored tasks: TTS and SASV, moving beyond conventional clean and controlled datasets.
  2. Foster interdisciplinary collaboration between spoofing generation (TTS) and detection (SASV) sides, encouraging the development of more integrated, robust, and realistic systems.


Why Participate


Challenge Tracks

There are two tracks in the WildSpoof challenge. Participation is open to all. Each team can participate in either task or both.

1. TTS Track

The Goal of the TTS track is to develop high-quality TTS systems using in-the-wild speech data. The training data is from the TITW dataset, and the evaluation set will be TITW-KSKT and TITW-KSUT (following the instructions here, we will add more detail soon).

2. SASV Track

The goal of the SASV track is to build robust SASV systems, utilizing source data from real-world conditions and spoofing attacks generated by TTS systems also trained on the same real-world data. This Spoofceleb dataset will be supported as the training set and test set for the SASV track.