Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders
Author:
Affiliation:
1. Kyoto University,Graduate School of Informatics,Kyoto,Japan
2. Sony AI,Tokyo,Japan
3. Sony Group Corporation,Tokyo,Japan
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10445798/10445803/10448429.pdf?arnumber=10448429
Reference40 articles.
1. An Overview of Noise-Robust Automatic Speech Recognition
2. Training speech enhancement systems with noisy speech datasets;Saito;arXiv,2021
3. Open-unmix for speech enhancement (umx se);Uhlich,2020
4. Recurrent neural networks for noise reduction in robust ASR
5. Ideal ratio mask estimation using deep neural networks for robust speech recognition
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1. Drift-DiffuSE: Diffusion Model with Learnable Drift Term for Speech Enhancement;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30
2. The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems;IEEE Signal Processing Letters;2024
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