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Language and noise transfer in speech enhancement generative adversarial network

Author
Pascual, S.; Park, M.; Serra, J.; Bonafonte, A.; Ahn, K.
Type of activity
Presentation of work at congresses
Name of edition
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing
Date of publication
2018
Presentation's date
2018-04-15
Book of congress proceedings
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing: proceedings: April 15-20, 2018 Calgary: Telus Convention Center: Calgary: Alberta, Canada
First page
5019
Last page
5023
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
DOI
https://doi.org/10.1109/ICASSP.2018.8462322 Open in new window
Repository
http://hdl.handle.net/2117/122808 Open in new window
URL
https://ieeexplore.ieee.org/document/8462322 Open in new window
Abstract
Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by fine-tuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective me...
Citation
Pascual, S., Park, M., Serra, J., Bonafonte, A., Ahn, K. Language and noise transfer in speech enhancement generative adversarial network. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 5019-5023.
Keywords
Deep learning, Generative adversarial networks, Speech enhancement, Transfer learning
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants