EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmark
arxiv(2024)
摘要
Speech emotion recognition (SER) is an important part of human-computer
interaction, receiving extensive attention from both industry and academia.
However, the current research field of SER has long suffered from the following
problems: 1) There are few reasonable and universal splits of the datasets,
making comparing different models and methods difficult. 2) No commonly used
benchmark covers numerous corpus and languages for researchers to refer to,
making reproduction a burden. In this paper, we propose EmoBox, an
out-of-the-box multilingual multi-corpus speech emotion recognition toolkit,
along with a benchmark for both intra-corpus and cross-corpus settings. For
intra-corpus settings, we carefully designed the data partitioning for
different datasets. For cross-corpus settings, we employ a foundation SER
model, emotion2vec, to mitigate annotation errors and obtain a test set that is
fully balanced in speakers and emotions distributions. Based on EmoBox, we
present the intra-corpus SER results of 10 pre-trained speech models on 32
emotion datasets with 14 languages, and the cross-corpus SER results on 4
datasets with the fully balanced test sets. To the best of our knowledge, this
is the largest SER benchmark, across language scopes and quantity scales. We
hope that our toolkit and benchmark can facilitate the research of SER in the
community.
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