Exploring Methods for the Automatic Detection of Errors in Manual Transcription

Autor:	Wang, Xiaofei, Yang, Jinyi, Li, Ruizhi, Sadhu, Samik, Hermansky, Hynek
Rok vydání:	2019
Předmět:	Computer Science - Computation and Language Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Submitted in Interspeech 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1904.04294 Zobrazit plný text záznamu View this record from Arxiv