Manipulating emotions for ground truth emotion analysis

Cross-speaker Emotion Transfer by Manipulating Speech Style Latents

In recent years, emotional text-to-speech has shown considerable progress. However, it requires a large amount of labeled data, which is not easily accessible. Even if it is possible to acquire an emotional speech dataset, there is still a limitation in controlling emotion intensity. In this work, we propose a novel method for cross-speaker emotion transfer and manipulation using vector arithmetic in latent style space. By leveraging only a few labeled samples, we generate emotional speech from reading-style speech without losing the speaker identity. Furthermore, emotion strength is readily c…

Measuring Emotions in the COVID-19 Real World Worry Dataset

The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling fur…

从文本到情感：揭示大语言模型的情感标注能力

From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs

本文探索了GPT4在情感标注中的潜力，发现其标注在多个数据集上比人工标注更受评估者偏好，且常用指标会低估其性能，并验证了GPT4用于标注过滤可改进模型训练。

Multi-Task Learning Framework for Extracting Emotion Cause Span and Entailment in Conversations

Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment…

GPT-4从第三人称视角模拟平均人类情感认知

GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective

本文研究GPT-4的情感推理能力，发现其从第三人称视角模拟普通人类的情感认知，在判断他人情绪时比自我归因更准确，表明普通观察者立场对下游应用更具相关性。

基于大语言模型的人机协同推理用于越南语语音情感识别

Human-Guided Reasoning with Large Language Models for Vietnamese Speech Emotion Recognition

本文提出一种融合人类知识的语音情感识别框架，通过LLM推理处理模糊样本，提升低资源环境下的情感分类性能，达到86.59%的准确率。