The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

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 …

The recently proposed Conformer model has become the de facto backbone model for
various downstream speech tasks based on its hybrid attention-convolution architecture that …

Abstract End-to-end Automatic Speech Recognition (ASR) systems based on neural
networks have seen large improvements in recent years. The availability of large scale hand …

This paper introduces a new multi-speaker English dataset for training text-to-speech
models. The dataset is based on LibriVox audiobooks and Project Gutenberg texts, both in …

The recently proposed Conformer architecture has shown state-of-the-art performances in
Automatic Speech Recog-nition by combining convolution with attention to model both local …

In recent years, extensive research has been conducted on the vulnerability of ASR systems,
revealing that black-box adversarial example attacks pose significant threats to real-world …

Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence,
which significantly reduces the inference speed at the cost of accuracy drop compared to …

The growth in online child exploitation material is a significant challenge for European Law
Enforcement Agencies (LEAs). One of the most important sources of such online information …

Error correction in automatic speech recognition (ASR) aims to correct those incorrect words
in sentences generated by ASR models. Since recent ASR models usually have low word …