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.
Fearless Steps Challenge Phase-3 (FSC P3): Advancing SLT for Unseen Channel and Mission Data Across NASA Apollo Audio
The Fearless Steps Challenge (FSC) initiative was designed to host a series of progressively complex tasks to promote advanced speech research across naturalistic “Big Data” corpora. The Center for Robust Speech Systems at UT-Dallas in collaboration with the National Institute of Standards and Technology (NIST) and Linguistic Data Consortium (LDC) conducted Phase-3 of the FSC series (FSC P3), with a focus on motivating speech and language technology (SLT) system generalizability across channel and mission diversity under the same training conditions as in Phase-2. The FSC P3 introduced 10 hours of previously unseen channel audio from Apollo-11 and 5 hours of novel audio from Apollo-13 to be evaluated over both previously established and newly introduced SLT tasks with streamlined tracks. This paper presents an overview of the newly introduced conversational analysis tracks, Apollo-13 data, and analysis of system performance for matched and mismatched challenge conditions. We also discuss the Phase-3 challenge results, evolution of system performance across the three Phases, and next steps in the Challenge Series.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- ISCA INTERSPEECH-2021
- Page Range or eLocation-ID:
- 986 to 990
- Sponsoring Org:
- National Science Foundation
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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.
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In this study, we propose to investigate triplet loss for the purpose of an alternative feature representation for ASR. We consider a general non-semantic speech representation, which is trained with a self-supervised criteria based on triplet loss called TRILL, for acoustic modeling to represent the acoustic characteristics of each audio. This strategy is then applied to the CHiME-4 corpus and CRSS-UTDallas Fearless Steps Corpus, with emphasis on the 100-hour challenge corpus which consists of 5 selected NASA Apollo-11 channels. An analysis of the extracted embeddings provides the foundation needed to characterize training utterances into distinct groups based on acoustic distinguishing properties. Moreover, we also demonstrate that triplet-loss based embedding performs better than i-Vector in acoustic modeling, confirming that the triplet loss is more effective than a speaker feature. With additional techniques such as pronunciation and silence probability modeling, plus multi-style training, we achieve a +5.42% and +3.18% relative WER improvement for the development and evaluation sets of the Fearless Steps Corpus. To explore generalization, we further test the same technique on the 1 channel track of CHiME-4 and observe a +11.90% relative WER improvement for real test data.