  Although deep learning-based end-to-end Automatic Speech Recognition (ASR)
has shown remarkable performance in recent years, it suffers severe performance
regression on test samples drawn from different data distributions. Test-time
Adaptation (TTA), previously explored in the computer vision area, aims to
adapt the model trained on source domains to yield better predictions for test
samples, often out-of-domain, without accessing the source data. Here, we
propose the Single-Utterance Test-time Adaptation (SUTA) framework for ASR,
which is the first TTA study on ASR to our best knowledge. The single-utterance
TTA is a more realistic setting that does not assume test data are sampled from
identical distribution and does not delay on-demand inference due to
pre-collection for the batch of adaptation data. SUTA consists of unsupervised
objectives with an efficient adaptation strategy. Empirical results demonstrate
that SUTA effectively improves the performance of the source ASR model
evaluated on multiple out-of-domain target corpora and in-domain test samples.
