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 …

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …

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 …

This document describes version 0.10 of TorchAudio: building blocks for machine learning
applications in the audio and speech processing domain. The objective of TorchAudio is to …

The goal of self-supervised learning (SSL) for automatic speech recognition (ASR) is to
learn good speech representations from a large amount of unlabeled speech for the …

Automatic speech recognition (ASR) is a crucial field of science due to its massive
applications that can be developed to help humans to improve their daily life tasks. Despite …

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 …

Arabic automatic speech recognition (ASR) methods with diacritics have the ability to be
integrated with other systems better than Arabic ASR methods without diacritics. In this work …

This paper introduces a novel Token-and-Duration Transducer (TDT) architecture for
sequence-to-sequence tasks. TDT extends conventional RNN-Transducer architectures by …

Modern wake word detection systems usually rely on neural networks for acoustic modeling.
Transformers has recently shown superior performance over LSTM and convolutional …