Emotion corpora are typically sampled based on keyword/hashtag search or by
asking study participants to generate textual instances. In any case, these
corpora are not uniform samples representing the entirety of a domain. We
hypothesize that this practice of data acquisition leads to unrealistic
correlations between overrepresented topics in these corpora that harm the
generalizability of models. Such topic bias could lead to wrong predictions for
instances like "I organized the service for my aunt's funeral." when funeral
events are over-represented for instances labeled with sadness, despite the
emotion of pride being more appropriate here. In this paper, we study this
topic bias both from the data and the modeling perspective. We first label a
set of emotion corpora automatically via topic modeling and show that emotions
in fact correlate with specific topics. Further, we see that emotion
classifiers are confounded by such topics. Finally, we show that the
established debiasing method of adversarial correction via gradient reversal
mitigates the issue. Our work points out issues with existing emotion corpora
and that more representative resources are required for fair evaluation of
models predicting affective concepts from text.

我们研究了情绪语料库中存在的主题偏差以及相关的建模方法，并发现通过对抗性修正可以缓解此问题。我们的研究指出现有情绪语料库存在问题，并且需要更具代表性的资源来进行情感概念从文本中预测的公平评估。

情感分类中的主题偏见

Topic Bias in Emotion Classification

In Speech Emotion Recognition (SER), textual data is often used alongside
audio signals to address their inherent variability. However, the reliance on
human annotated text in most research hinders the development of practical SER
systems. To overcome this challenge, we investigate how Automatic Speech
Recognition (ASR) performs on emotional speech by analyzing the ASR performance
on emotion corpora and examining the distribution of word errors and confidence
scores in ASR transcripts to gain insight into how emotion affects ASR. We
utilize four ASR systems, namely Kaldi ASR, wav2vec, Conformer, and Whisper,
and three corpora: IEMOCAP, MOSI, and MELD to ensure generalizability.
Additionally, we conduct text-based SER on ASR transcripts with increasing word
error rates to investigate how ASR affects SER. The objective of this study is
to uncover the relationship and mutual impact of ASR and SER, in order to
facilitate ASR adaptation to emotional speech and the use of SER in real world.

研究利用自动语音识别系统分析情感语音，以及在转录情感语音时出现的错误，探讨自动语音识别对情感语音的适应性，进而促进情感语音的实际应用。