In this paper, we present TED-LIUM release 3 corpus dedicated to speech recognition in English, that multiplies by more than two the available data to train acoustic models in comparison with TED-LIUM 2. We present the recent development on Automatic Speech Recognition (ASR) systems in comparison with the two previous releases of the TED-LIUM Corpus from 2012 and 2014. We demonstrate that, passing from 207 to 452 hours of transcribed speech training data is really more useful for end-to-end ASR systems than for HMM-based state-of-the-art ones, even if the HMM-based ASR system still outperforms end-to-end ASR system when the size of audio training data is 452 hours, with respectively a Word Error Rate (WER) of 6.6% and 13.7%. Last, we propose two repartitions of the TED-LIUM release 3 corpus: the legacy one that is the same as the one existing in release 2, and a new one, calibrated and designed to make experiments on speaker adaptation. Like the two first releases, TED-LIUM 3 corpus will be freely available for the research community.

本文介绍TED-LIUM发布的第三个语音识别数据集，主要是为了增加训练声学模型的可用数据，比TED-LIUM 2多出一倍以上的语料。研究表明，相比2012年和2014年发布的数据集，通过提高训练数据量，对于端到端的ASR系统更为有效。同时，本文还提出了根据说话人自适应进行实验的两组TED-LIUM release 3 corpus数据分配计划，并且将其免费提供给研究社区。

TED-LIUM 3: 基于增加数据和语料库重新分配的说话人自适应实验