The purpose of emotion recognition in conversation (ERC) is to identify the
emotion category of an utterance based on contextual information. Previous ERC
methods relied on simple connections for cross-modal fusion and ignored the
information differences between modalities, resulting in the model being unable
to focus on modality-specific emotional information. At the same time, the
shared information between modalities was not processed to generate emotions.
Information redundancy problem. To overcome these limitations, we propose a
cross-modal fusion emotion prediction network based on vector connections. The
network mainly includes two stages: the multi-modal feature fusion stage based
on connection vectors and the emotion classification stage based on fused
features. Furthermore, we design a supervised inter-class contrastive learning
module based on emotion labels. Experimental results confirm the effectiveness
of the proposed method, demonstrating excellent performance on the IEMOCAP and
MELD datasets.

基于向量连接的跨模态融合情感预测网络，包括多模态特征融合阶段和基于融合特征的情感分类阶段，同时设计了基于情感标签的监督式跨类对比学习模块，实验证实了该方法的有效性，并在 IEMOCAP 和 MELD 数据集上展现出优异的性能。

通过情感跨模态融合和类间对比学习提升对话中的情感识别

Enhancing Emotion Recognition in Conversation through Emotional  Cross-Modal Fusion and Inter-class Contrastive Learning

Speech data carries a range of personal information, such as the speaker's
identity and emotional state. These attributes can be used for malicious
purposes. With the development of virtual assistants, a new generation of
privacy threats has emerged. Current studies have addressed the topic of
preserving speech privacy. One of them, the VoicePrivacy initiative aims to
promote the development of privacy preservation tools for speech technology.
The task selected for the VoicePrivacy 2020 Challenge (VPC) is about speaker
anonymization. The goal is to hide the source speaker's identity while
preserving the linguistic information. The baseline of the VPC makes use of a
voice conversion. This paper studies the impact of the speaker anonymization
baseline system of the VPC on emotional information present in speech
utterances. Evaluation is performed following the VPC rules regarding the
attackers' knowledge about the anonymization system. Our results show that the
VPC baseline system does not suppress speakers' emotions against informed
attackers. When comparing anonymized speech to original speech, the emotion
recognition performance is degraded by 15\% relative to IEMOCAP data, similar
to the degradation observed for automatic speech recognition used to evaluate
the preservation of the linguistic information.

该研究研究了在语音隐私保护的 VoicePrivacy 2020 Challenge 中对说话人匿名化基线系统对语音情感信息的影响，结果表明该系统对知情攻击者未能抑制说话人的情感，且匿名化后的语音相比原始语音情感识别性能下降了 15%，与其保留语言信息的评估方式使用自动语音识别得到的结果类似。

评估说话人匿名在情感语音上的效果

Evaluation of Speaker Anonymization on Emotional Speech

Identifying misinformation is increasingly being recognized as an important
computational task with high potential social impact. Misinformation and fake
contents are injected into almost every domain of news including politics,
health, science, business, etc., among which, the fakeness in health domain
pose serious adverse effects to scare or harm the society. Misinformation
contains scientific claims or content from social media exaggerated with strong
emotion content to attract eyeballs. In this paper, we consider the utility of
the affective character of news articles for fake news identification in the
health domain and present evidence that emotion cognizant representations are
significantly more suited for the task. We outline a technique to leverage
emotion intensity lexicons to develop emotionized text representations, and
evaluate the utility of such a representation for identifying fake news
relating to health in various supervised and unsupervised scenarios. The
consistent and significant empirical gains that we observe over a range of
technique types and parameter settings establish the utility of the emotional
information in news articles, an often overlooked aspect, for the task of
misinformation identification in the health domain.

本研究考虑了新闻文章情感信息对健康领域虚假新闻识别的作用，利用情感强度词库开发情感化文本表示技术，在多种监督和无监督情况下评估了该表示技术的实用性，结果表明情感信息的使用可以显著提高健康领域识别虚假信息的准确率。

情感认知提高健康虚假新闻辨别

Emotion Cognizance Improves Health Fake News Identification

Over the last million years, human language has emerged and evolved as a
fundamental instrument of social communication and semiotic representation.
People use language in part to convey emotional information, leading to the
central and contingent questions: (1) What is the emotional spectrum of natural
language? and (2) Are natural languages neutrally, positively, or negatively
biased? Here, we report that the human-perceived positivity of over 10,000 of
the most frequently used English words exhibits a clear positive bias. More
deeply, we characterize and quantify distributions of word positivity for four
large and distinct corpora, demonstrating that their form is broadly invariant
with respect to frequency of word use.

本文研究自然语言情感色彩的积极偏向性及其形式分布规律，并对四个大型语料库的情感词性进行量化和分析。