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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. 

Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The i-vectors extracted from different segments of same speaker are correlated. We model this correlation with a Markov Random Field (MRF) network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse Covariance (TIC) matrix to represent the MRF correlation network for each speaker. This approaches captures the sequential structure of i-vectors (or equivalent speaker turns) belonging to same speaker in an audio stream. A variant of standard Expectation Maximization (EM) algorithm is adopted for deriving closed-form solution using dynamic programming (DP) and the alternating direction method of multiplier (ADMM). Our diarization system has four steps: (1) ground-truth segmentation; (2) i-vector extraction; (3) post-processing (mean subtraction, principal component analysis, and length-normalization) ; and (4) proposed speaker clustering. We employ cosine K-means and movMF speaker clustering as baseline approaches. Our evaluation data is derived from: (i) CRSS-PLTL corpus, and (ii) two meetings subset of the AMI corpus. Relative reduction in diarization error rate (DER) for CRSS-PLTL corpus is 43.22% using the proposed advancements as compared to baseline. For AMI meetings IS1000a and IS1003b, relative DER reduction is 29.37% and 9.21%, respectively. 

The 2019 FEARLESS STEPS (FS-1) Challenge is an initial step to motivate a streamlined and collaborative effort from the speech and language community towards addressing massive naturalistic audio, the first of its kind. The Fearless Steps Corpus is a collection of 19,000 hours of multi-channel recordings of spontaneous speech from over 450 speakers under multiple noise conditions. A majority of the Apollo Missions original analog data is unlabeled and has thus far motivated the development of both unsupervised and semi-supervised strategies. This edition of the challenge encourages the development of core speech and language technology systems for data with limited ground-truth / low resource availability and is intended to serve as the “First Step” towards extracting high-level information from such massive unlabeled corpora. In conjunction with the Challenge, 11,000 hours of synchronized 30-channel Apollo-11 audio data has also been released to the public by CRSS-UTDallas. We describe in this paper the Fearless Steps Corpus, Challenge Tasks, their associated baseline systems, and results. In conclusion, we also provide insights gained by the CRSS-UTDallas team during the inaugural Fearless Steps Challenge.