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A review of deep learning techniques for speech processing
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 …
learning. The use of multiple processing layers has enabled the creation of models capable …
A review of speaker diarization: Recent advances with deep learning
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 …
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
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 …
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
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 …
speech enhancement, where deep neural networks (DNNs) are used to predict the real and …
Asteroid: the PyTorch-based audio source separation toolkit for researchers
This paper describes Asteroid, the PyTorch-based audio source separation toolkit for
researchers. Inspired by the most successful neural source separation systems, it provides …
researchers. Inspired by the most successful neural source separation systems, it provides …