Swin-Transformer has demonstrated remarkable success in computer vision by
leveraging its hierarchical feature representation based on Transformer. In
speech signals, emotional information is distributed across different scales of
speech features, e.\,g., word, phrase, and utterance. Drawing above
inspiration, this paper presents a hierarchical speech Transformer with shifted
windows to aggregate multi-scale emotion features for speech emotion
recognition (SER), called Speech Swin-Transformer. Specifically, we first
divide the speech spectrogram into segment-level patches in the time domain,
composed of multiple frame patches. These segment-level patches are then
encoded using a stack of Swin blocks, in which a local window Transformer is
utilized to explore local inter-frame emotional information across frame
patches of each segment patch. After that, we also design a shifted window
Transformer to compensate for patch correlations near the boundaries of segment
patches. Finally, we employ a patch merging operation to aggregate
segment-level emotional features for hierarchical speech representation by
expanding the receptive field of Transformer from frame-level to segment-level.
Experimental results demonstrate that our proposed Speech Swin-Transformer
outperforms the state-of-the-art methods.

该研究提出了一种基于分层性 Speech Swin-Transformer 的声音情感识别方法，通过分级的窗口聚合多尺度情感特征，将 Transformer 的感知范围从帧级扩展到片段级，实验结果表明该方法优于现有的技术。

语音 Swin-Transformer: 探索一种具有平移窗口的分层 Transformer 用于语音情感识别

Speech Swin-Transformer: Exploring a Hierarchical Transformer with  Shifted Windows for Speech Emotion Recognition

Past work on personality detection has shown that frequency of lexical
categories such as first person pronouns, past tense verbs, and sentiment words
have significant correlations with personality traits. In this paper, for the
first time, we show that fine affect (emotion) categories such as that of
excitement, guilt, yearning, and admiration are significant indicators of
personality. Additionally, we perform experiments to show that the gains
provided by the fine affect categories are not obtained by using coarse affect
categories alone or with specificity features alone. We employ these features
in five SVM classifiers for detecting five personality traits through essays.
We find that the use of fine emotion features leads to statistically
significant improvement over a competitive baseline, whereas the use of coarse
affect and specificity features does not.

该论文表明，情感的精细类别是人格的重要指标，使用细致的情感特征可以显著提高基于文章的人格检测准确率。