Exploring Methods for the Automatic Detection of Errors in Manual Transcription

Abstract: 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 LLM and acoustic model, two approaches of automatic error detection in the transcriptions have been explored in this work. Previous study using a biased LLM approach, relying on a strong transcription-dependent LLM, 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.