  Quality of data plays an important role in most deep learning tasks. In the
speech community, transcription of speech recording is indispensable. Since the
transcription is usually generated artificially, automatically finding errors
in manual transcriptions not only saves time and labors but benefits the
performance of tasks that need the training process. Inspired by the success of
hybrid automatic speech recognition using both language model and acoustic
model, two approaches of automatic error detection in the transcriptions have
been explored in this work. Previous study using a biased language model
approach, relying on a strong transcription-dependent language model, has been
reviewed. In this work, we propose a novel acoustic model based approach,
focusing on the phonetic sequence of speech. Both methods have been evaluated
on a completely real dataset, which was originally transcribed with errors and
strictly corrected manually afterwards.
