LSSED: A Large-Scale Dataset and Benchmark for Speech Emotion Recognition

Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI). However, current existing small-scale databases have limited the development of related research. In this paper, we present LSSED, a challenging large-scale english speech emotion dataset, w...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 641 - 645
Main Authors Fan, Weiquan, Xu, Xiangmin, Xing, Xiaofen, Chen, Weidong, Huang, Dongyan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2021
Subjects
Analytical models
dataset
deep learning
Emotion recognition
Human computer interaction
Mental health
pretrained model
Signal processing algorithms
speech emotion recognition
Speech recognition
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Summary:Speech emotion recognition is a vital contributor to the next generation of human-computer interaction (HCI). However, current existing small-scale databases have limited the development of related research. In this paper, we present LSSED, a challenging large-scale english speech emotion dataset, which has data collected from 820 subjects to simulate real- world distribution. In addition, we release some pre-trained models based on LSSED, which can not only promote the development of speech emotion recognition, but can also be transferred to related downstream tasks such as mental health analysis where data is extremely difficult to collect. Finally, our experiments show the necessity of large-scale datasets and the effectiveness of pre-trained models. The dateset will be released on https://github.com/tobefans/LSSED.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414542