EmoTale：丹麦语的表演式语音情感数据集
EmoTale: An Enacted Speech-emotion Dataset in Danish
AI总结：
针对丹麦语缺乏功能性情感语音数据集的问题，提出含丹麦语和英语表演式情感标注的EmoTale语料库，用自监督语音模型嵌入开发SER模型验证其有效性，最佳模型UAR达64.1%且与DES性能相当。
摘要：
虽然常用语言已有多个情感语音语料库，但小语种（如丹麦语）缺乏功能性数据集。据我们所知，1997年发布的丹麦情感语音库（DES）是唯一的丹麦情感语音数据库。我们提出EmoTale，一个包含丹麦语和英语语音录音及其关联表演式情感标注的语料库。我们通过使用语音情感识别（SER）模型研究并展示其预测能力，验证了该数据集的有效性。我们利用自监督语音模型（SSLM）嵌入和openSMILE特征提取器，为EmoTale和参考数据集开发SER模型，发现嵌入优于手工特征。最佳模型在EmoTale语料库上通过留一说话人交叉验证实现64.1%的无加权平均召回率（UAR），与DES上的性能相当。
英文摘要：
While multiple emotional speech corpora exist for commonly spoken languages, there is a lack of functional datasets for smaller (spoken) languages, such as Danish. To our knowledge, Danish Emotional Speech (DES), published in 1997, is the only other database of Danish emotional speech. We present EmoTale; a corpus comprising Danish and English speech recordings with their associated enacted emotion annotations. We demonstrate the validity of the dataset by investigating and presenting its predictive power using speech emotion recognition (SER) models. We develop SER models for EmoTale and the reference datasets using self-supervised speech model (SSLM) embeddings and the openSMILE feature extractor. We find the embeddings superior to the hand-crafted features. The best model achieves an unweighted average recall (UAR) of 64.1% on the EmoTale corpus using leave-one-speaker-out cross-validation, comparable to the performance on DES.
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