  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.
