  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.
