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 …

Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …

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 …

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to
leverage useful information contained in multiple related tasks to help improve the …

A field that has directly benefited from the recent advances in deep learning is automatic
speech recognition (ASR). Despite the great achievements of the past decades, however, a …

Despite the overwhelming success of deep learning in various speech processing tasks, the
problem of separating simultaneous speakers in a mixture remains challenging. Two major …

Diffusion models have recently shown promising results for difficult enhancement tasks such
as the conditional and unconditional restoration of natural images and audio signals. In this …

Speech enhancement model is used to map a noisy speech to a clean speech. In the
training stage, an objective function is often adopted to optimize the model parameters …

Despite the recent success of deep learning for many speech processing tasks, single-
microphone, speaker-independent speech separation remains challenging for two main …

Multichannel automatic speech recognition (ASR) systems commonly separate speech
enhancement, including localization, beamforming, and postfiltering, from acoustic …