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 present ESPnet-ST, which is designed for the quick development of speech-to-speech
translation systems in a single framework. ESPnet-ST is a new project inside end-to-end …

We present Espresso, an open-source, modular, extensible end-to-end neural automatic
speech recognition (ASR) toolkit based on the deep learning library PyTorch and the …

Connectionist Temporal Classification (CTC) is a widely used approach for automatic
speech recognition (ASR) that performs conditionally independent monotonic alignment …

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 …

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) …

This paper addresses end-to-end automatic speech recognition (ASR) for long audio
recordings such as lecture and conversational speeches. Most end-to-end ASR models are …

This paper integrates a voice activity detection (VAD) function with end-to-end automatic
speech recognition toward an online speech interface and transcribing very long audio …

Unlike the traditional model, the end-to-end (E2E) ASR model does not require speech
information such as a pronunciation dictionary, and its system is built through a single neural …

End-to-end approaches for sequence tasks are becoming increasingly popular. Yet for
complex sequence tasks, like speech translation, systems that cascade several models …