  High-quality human transcription is essential for training and improving
Automatic Speech Recognition (ASR) models. Recent study~\cite{libricrowd} has
found that every 1% worse transcription Word Error Rate (WER) increases
approximately 2% ASR WER by using the transcriptions to train ASR models.
Transcription errors are inevitable for even highly-trained annotators.
However, few studies have explored human transcription correction. Error
correction methods for other problems, such as ASR error correction and
grammatical error correction, do not perform sufficiently for this problem.
Therefore, we propose HTEC for Human Transcription Error Correction. HTEC
consists of two stages: Trans-Checker, an error detection model that predicts
and masks erroneous words, and Trans-Filler, a sequence-to-sequence generative
model that fills masked positions. We propose a holistic list of correction
operations, including four novel operations handling deletion errors. We
further propose a variant of embeddings that incorporates phoneme information
into the input of the transformer. HTEC outperforms other methods by a large
margin and surpasses human annotators by 2.2% to 4.5% in WER. Finally, we
deployed HTEC to assist human annotators and showed HTEC is particularly
effective as a co-pilot, which improves transcription quality by 15.1% without
sacrificing transcription velocity.
