  In this study, we present recent developments of models trained with the
RNN-T loss in ESPnet. It involves the use of various architectures such as
recently proposed Conformer, multi-task learning with different auxiliary
criteria and multiple decoding strategies, including our own proposition.
Through experiments and benchmarks, we show that our proposed systems can be
competitive against other state-of-art systems on well-known datasets such as
LibriSpeech and AISHELL-1. Additionally, we demonstrate that these models are
promising against other already implemented systems in ESPnet in regards to
both performance and decoding speed, enabling the possibility to have powerful
systems for a streaming task. With these additions, we hope to expand the
usefulness of the ESPnet toolkit for the research community and also give tools
for the ASR industry to deploy our systems in realistic and production
environments.

Audio and Speech Processing;Sound