Common latent representation learning for low-resourced spoken language identification-Reference-Cited by-同舟云学术

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Common latent representation learning for low-resourced spoken language identification

Published:2023-09-26 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Chen Chen^ORCID,Bu Yulin,Chen Yong,Chen Deyun

Funder

National Natural Science Foundation of China

Postdoctoral Research Foundation of China

Natural Science Foundation of Heilongjiang Province of China

Heilongjiang Provincial Postdoctoral Science Foundation

Fundamental Research Foundation for Universities of Heilongjiang Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-16865-x.pdf

Reference53 articles.

1. Biswas M, Rahaman S, Ahmadian A et al (2022) Automatic spoken language identification using MFCC based time series features. Multimedia Tools and Applications, pp 1–31. https://doi.org/10.1007/s11042-021-11439-1

2. Albadr MAA, Tiun S, Ayob M et al (2023) Grey wolf optimization-extreme learning machine for automatic spoken language identification. Multimedia Tools and Applications, pp 1–27. https://doi.org/10.1007/s11042-023-14473-3

3. Romero D, D’Haro LF, Estecha-Garitagoitia M et al (2022) Phonotactic Language Recognition Using A Universal Phoneme Recognizer and A Transformer Architecture. ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 6872–6876. https://doi.org/10.1109/icassp43922.2022.9746459

4. Tjandra A, Choudhury DG, Zhang F et al (2022) Improved language identification through cross-lingual self-supervised learning. ICASSP 2022 IEEE Int Conf Acoust, Speech Sig Process (ICASSP).IEEE, pp 6877–6881. https://doi.org/10.1109/icassp43922.2022.9747667

5. Punjabi S, Arsikere H, Raeesy Z et al (2021) Joint ASR and language identification using RNN-T: An efficient approach to dynamic language switching. ICASSP 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 7218–7222. https://doi.org/10.1109/icassp39728.2021.9413734

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