A review of deep learning techniques for speech processing

A Mehrish, N Majumder, R Bharadwaj, R Mihalcea… - Information …, 2023 - Elsevier
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 …

A review of speaker diarization: Recent advances with deep learning

TJ Park, N Kanda, D Dimitriadis, KJ Han… - Computer Speech & …, 2022 - Elsevier
Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

[HTML][HTML] A survey of sound source localization with deep learning methods

PA Grumiaux, S Kitić, L Girin, A Guérin - The Journal of the Acoustical …, 2022 - pubs.aip.org
This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …

CHiME-6 challenge: Tackling multispeaker speech recognition for unsegmented recordings

S Watanabe, M Mandel, J Barker, E Vincent… - ar** for single-and multi-channel speech enhancement and robust ASR
ZQ Wang, P Wang, DL Wang - IEEE/ACM transactions on …, 2020 - ieeexplore.ieee.org
This study proposes a complex spectral map** approach for single-and multi-channel
speech enhancement, where deep neural networks (DNNs) are used to predict the real and …

Asteroid: the PyTorch-based audio source separation toolkit for researchers

M Pariente, S Cornell, J Cosentino… - arxiv preprint arxiv …, 2020 - arxiv.org
This paper describes Asteroid, the PyTorch-based audio source separation toolkit for
researchers. Inspired by the most successful neural source separation systems, it provides …