TED-LIUM 3: Twice as Much Data and Corpus Repartition for Experiments on Speaker Adaptation

In this paper, we present TED-LIUM release 3 corpus (TED-LIUM 3 is available on https://lium.univ-lemans.fr/ted-lium3/) dedicated to speech recognition in English, which multiplies the available data to train acoustic models in comparison with TED-LIUM 2, by a factor of more than two. 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 h of transcribed speech training data is really more useful for end-to-end ASR systems than for HMM-based state-of-the-art ones. This is the case even if the HMM-based ASR system still outperforms the end-to-end ASR system when the size of audio training data is 452 h, with aWord Error Rate (WER) of 6.7% and 13.7%, respectively. Finally, we propose two repartitions of the TED-LIUM release 3 corpus: the legacy repartition that is the same as that existing in release 2, and a new repartition, calibrated and designed to make experiments on speaker adaptation. Similar to the two first releases, TED-LIUM 3 corpus will be freely available for the research community.