Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

We investigate a set of techniques for RNN Transducers (RNN-Ts) that were instrumental in
lowering the word error rate on three different tasks (Switchboard 300 hours, conversational …

We improve on the popular conformer architecture by replacing the depthwise temporal
convolutions with diagonal state space (DSS) models. DSS is a recently introduced variant …

This paper describes the recent development of ESPnet (https://github. com/espnet/espnet),
an end-to-end speech processing toolkit. This project was initiated in December 2017 to …

End-to-end training of recurrent neural network transducers (RNN-Ts) does not require
frame-level alignments between audio and output symbols. Because of that, the posterior …

The RNN transducer is a promising end-to-end model candidate. We compare the original
training criterion with the full marginalization over all alignments, to the commonly used …

The Transformer self-attention network has shown promising performance as an alternative
to recurrent neural networks in end-to-end (E2E) automatic speech recognition (ASR) …

In this study, we present recent developments of models trained with the RNN-T loss in
ESPnet. It involves the use of various archi-tectures such as recently proposed Conformer …

ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the
broadening interests of the spoken language translation community. ESPnet-ST-v2 supports …