Lightweight real-time recurrent models for speech enhancement and automatic speech recognition
Traditional recurrent neural networks (RNNs) encounter difficulty in capturing long-term temporal dependencies. However, lightweight recurrent models for speech enhancement are important to improve noisy speech, while being computationally efficient and able to capture long-term temporal dependencie...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English English English |
| Published: |
Universidad Internacional de la Rioja
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/113757/1/113757_Lightweight%20real-time%20recurrent%20models.pdf http://irep.iium.edu.my/113757/2/113757_Lightweight%20real-time%20recurrent%20models_SCOPUS.pdf http://irep.iium.edu.my/113757/3/113757_Lightweight%20real-time%20recurrent%20models_WOS.pdf http://irep.iium.edu.my/113757/ https://www.ijimai.org/journal/bibcite/reference/3450 |
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http://irep.iium.edu.my/113757/1/113757_Lightweight%20real-time%20recurrent%20models.pdfhttp://irep.iium.edu.my/113757/2/113757_Lightweight%20real-time%20recurrent%20models_SCOPUS.pdf
http://irep.iium.edu.my/113757/3/113757_Lightweight%20real-time%20recurrent%20models_WOS.pdf
http://irep.iium.edu.my/113757/
https://www.ijimai.org/journal/bibcite/reference/3450