Ensemble deep learning with HuBERT for speech emotion recognition

被引：2

作者：

Yang, Janghoon ^{[1
]}

机构：

[1] Seoul Media Inst Technol, AI Software Engn, Seoul, South Korea

来源：

2023 IEEE 17TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING, ICSC | 2023年

关键词：

transformer; ensemble model; HuBERT; speech emotion recognition;

D O I：

10.1109/ICSC56153.2023.00032

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a deep learning model for speech emotion recognition which consists of HuBERT, statistical feature extraction, and transformer. Several different ensemble methods are also considered. The proposed method is shown to achieve a test accuracy of 68.31% for CREMA-D. The ensemble method of summing the outputs of multiple models and determining the emotion is shown to achieve an accuracy of 70.24% with the ensemble of 5 models which is close to the state of the art (SOTA) performance with CREMA-D.

引用

页码：153 / 154

页数：2

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