WildSpoof Challenge Evaluation Plan
This addresses the need for more realistic and robust speech systems in real-world scenarios, but it is incremental as it builds on existing challenge frameworks.
The WildSpoof Challenge tackles the problem of advancing speech processing with in-the-wild data by organizing two parallel tracks for text-to-speech synthesis to generate spoofed speech and spoofing-robust automatic speaker verification to detect it, aiming to move beyond clean datasets and foster interdisciplinary collaboration.
The WildSpoof Challenge aims to advance the use of in-the-wild data in two intertwined speech processing tasks. It consists of two parallel tracks: (1) Text-to-Speech (TTS) synthesis for generating spoofed speech, and (2) Spoofing-robust Automatic Speaker Verification (SASV) for detecting spoofed speech. While the organizers coordinate both tracks and define the data protocols, participants treat them as separate and independent tasks. The primary objectives of the challenge are: (i) to promote the use of in-the-wild data for both TTS and SASV, moving beyond conventional clean and controlled datasets and considering real-world scenarios; and (ii) to encourage interdisciplinary collaboration between the spoofing generation (TTS) and spoofing detection (SASV) communities, thereby fostering the development of more integrated, robust, and realistic systems.