  The transcription quality of automatic speech recognition (ASR) systems
degrades significantly when transcribing audios coming from unseen domains. We
propose an unsupervised error correction method for unsupervised ASR domain
adaption, aiming to recover transcription errors caused by domain mismatch.
Unlike existing correction methods that rely on transcribed audios for
training, our approach requires only unlabeled data of the target domains in
which a pseudo-labeling technique is applied to generate correction training
samples. To reduce over-fitting to the pseudo data, we also propose an
encoder-decoder correction model that can take into account additional
information such as dialogue context and acoustic features. Experiment results
show that our method obtains a significant word error rate (WER) reduction over
non-adapted ASR systems. The correction model can also be applied on top of
other adaptation approaches to bring an additional improvement of 10%
relatively.
