Diversity subspace generation based on feature selection for speech emotion recognition-Reference-Cited by-同舟云学术

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Diversity subspace generation based on feature selection for speech emotion recognition

Published:2023-08-17 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Ye Qing,Sun Yaxin

Funder

Natural Science Foundation of Zhejiang Province

Science and Technology Plan Project of Wenzhou, China

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/s11042-023-16465-9.pdf

Reference72 articles.

1. Anagnostopoulos T, Skourlas C (2014) Ensemble majority voting classifier for speech emotion recognition and prediction. J Syst Inf Technol 16(3):222–232

2. Anuragi A, Pachori RB, Sisodia DS (2022) EEG-based cross-subject emotion recognition using Fourier-Bessel series expansion based empirical wavelet transform and NCA feature selection method. Inform Sci 610:508–524

3. Badshah AM, Ahmad J, Lee MY, Baik SW (2016) Divide-and-Conquer based Ensemble to Spot Emotions in Speech using MFCC and Random Forest. Int Int Conf Concert Converg:1–8

4. Bandela SR, Kishore Kumar T (2021) Unsupervised feature selection and NMF de-noising for robust speech emotion recognition. Appl Acoust 172(15):1–15

5. Bastanfard A (2023) Alireza Abbasian. Speech emotion recognition in Persian based on stacked autoencoder by comparing local and global features, Multimedia Tools and Applications in Press

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