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。