Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement
- DOI
- 10.1016/j.jksuci.2023.02.007
- Published
- 2023-03
- Container
- Journal of King Saud University - Computer and Information Sciences
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jksuci.2023.02.007,
title = {Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement},
author = {Da-Hee Yang and Joon-Hyuk Chang},
year = {2023},
journal = {Journal of King Saud University - Computer and Information Sciences},
doi = {10.1016/j.jksuci.2023.02.007},
url = {https://doi.org/10.1016/j.jksuci.2023.02.007}
}RIS
TY - JOUR TI - Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement AU - Da-Hee Yang AU - Joon-Hyuk Chang PY - 2023 JO - Journal of King Saud University - Computer and Information Sciences DO - 10.1016/j.jksuci.2023.02.007 UR - https://doi.org/10.1016/j.jksuci.2023.02.007 ER -
APA
Yang, D., & Chang, J. (2023). Attention-based latent features for jointly trained end-to-end automatic speech recognition with modified speech enhancement. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2023.02.007
Source records
- crossref · retrieved 2026-11-10T13:59:40.594Z