FEARLESS STEPS Challenge (FS-2): Supervised Learning with Massive Naturalistic Apollo Data
This work addresses the need for robust speech and language technology in handling massive, real-world audio data, though it is incremental as it builds on previous challenge phases.
The paper presents the FEARLESS STEPS Challenge (FS-2), which tackles the problem of developing supervised learning algorithms for multi-party and multi-stream naturalistic audio using 19,000 hours of digitized Apollo mission data, resulting in revised baseline results and insights from community feedback.
The Fearless Steps Initiative by UTDallas-CRSS led to the digitization, recovery, and diarization of 19,000 hours of original analog audio data, as well as the development of algorithms to extract meaningful information from this multi-channel naturalistic data resource. The 2020 FEARLESS STEPS (FS-2) Challenge is the second annual challenge held for the Speech and Language Technology community to motivate supervised learning algorithm development for multi-party and multi-stream naturalistic audio. In this paper, we present an overview of the challenge sub-tasks, data, performance metrics, and lessons learned from Phase-2 of the Fearless Steps Challenge (FS-2). We present advancements made in FS-2 through extensive community outreach and feedback. We describe innovations in the challenge corpus development, and present revised baseline results. We finally discuss the challenge outcome and general trends in system development across both phases (Phase FS-1 Unsupervised, and Phase FS-2 Supervised) of the challenge, and its continuation into multi-channel challenge tasks for the upcoming Fearless Steps Challenge Phase-3.