Towards efficient recurrent architectures: a deep LSTM neural network applied to speech enhancement and recognition
Long short-term memory (LSTM) has proven effective in modeling sequential data. However, it may encounter challenges in accurately capturing long-term temporal dependencies. LSTM plays a central role in speech enhancement by effectively modeling and capturing temporal dependencies in speech signals....
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English English English |
| Published: |
Springer Nature
2024
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/112153/1/112153_Towards%20efficient%20recurrent%20architectures.pdf http://irep.iium.edu.my/112153/2/112153_Towards%20efficient%20recurrent%20architectures_SCOPUS.pdf http://irep.iium.edu.my/112153/3/112153_Towards%20efficient%20recurrent%20architectures_WOS.pdf http://irep.iium.edu.my/112153/ https://link.springer.com/article/10.1007/s12559-024-10288-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|