WildSpoof Challenge Evaluation Plan

TL;DR

The WildSpoof Challenge promotes the development of robust speech synthesis and spoof detection systems using real-world data, encouraging collaboration between TTS and SASV communities to improve security and realism in speech processing.

Contribution

It introduces a new challenge framework focusing on in-the-wild data for TTS and SASV, fostering interdisciplinary collaboration and advancing practical speech spoofing and detection methods.

Findings

Promotes use of real-world data in speech spoofing and detection

Encourages collaboration between TTS and SASV communities

Aims to develop more robust, realistic speech processing systems

Abstract

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.