Investigating Graph-based Features for Speech Emotion Recognition-Reference-Cited by-同舟云学术

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Investigating Graph-based Features for Speech Emotion Recognition

Published:2022-09-27 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
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Author:

Pentari Anastasia¹,Kafentzis George²,Tsiknakis Manolis³

Affiliation:

1. Foundation for Research and Technology-Hellas,Computational BioMedicine Laboratory

2. University of Crete,Computer Science Department

3. Hellenic Mediterranean University,Biomedical Informatics and eHealth,Department of Electrical and Computer Engineering

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9926747/9926739/09926795.pdf?arnumber=9926795

Reference39 articles.

1. Visualizing High-Dimensional Data Using t-SNE;van der maaten;Journal of Machine Learning Research,2008

2. Speech Emotion Recognition Using Deep Learning Techniques: A Review

3. Emotion recognition in low-resource settings: An evaluation of automatic feature selection methods

4. The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Speech emotion recognition via graph-based representations;Scientific Reports;2024-02-23

2. A Visibility Graph Approach for Multi-Stage Classification of Parkinson’s Disease Using Multimodal Data;IEEE Access;2024

3. Multi-channel CNN-based emotion recognition using recurrence plot representations of speech;2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM);2023-12-05

4. Speech emotion recognition based on Graph-LSTM neural network;EURASIP Journal on Audio, Speech, and Music Processing;2023-10-11

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