LSSED: a large-scale dataset and benchmark for speech emotion recognition
This work addresses the need for large-scale datasets to advance speech emotion recognition for human-computer interaction, though it is incremental as it builds on existing data collection methods.
The authors tackled the problem of limited small-scale databases in speech emotion recognition by introducing LSSED, a large-scale English dataset with data from 820 subjects, and showed that pre-trained models based on it are effective for promoting research and downstream tasks like mental health analysis.
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.