MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge

• URL: http://arxiv.org/abs/2505.24493v1
• Date: Fri, 30 May 2025 11:45:36 GMT
• Title: MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge
• Authors: Xin Jing, Jiadong Wang, Iosif Tsangko, Andreas Triantafyllopoulos, Björn W. Schuller,
• Abstract summary: We apply GPT-4o to annotate a multimodal dataset collected from the sitcom Friends.<n>By crafting structured text prompts, our methodology capitalizes on the knowledge GPT-4o has accumulated during its training.<n>We demonstrate the effectiveness of MELT by fine-tuning four self-supervised learning (SSL) backbones and assessing speech emotion recognition performance.
• Score: 45.04480661233793
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although speech emotion recognition (SER) has advanced significantly with deep learning, annotation remains a major hurdle. Human annotation is not only costly but also subject to inconsistencies annotators often have different preferences and may lack the necessary contextual knowledge, which can lead to varied and inaccurate labels. Meanwhile, Large Language Models (LLMs) have emerged as a scalable alternative for annotating text data. However, the potential of LLMs to perform emotional speech data annotation without human supervision has yet to be thoroughly investigated. To address these problems, we apply GPT-4o to annotate a multimodal dataset collected from the sitcom Friends, using only textual cues as inputs. By crafting structured text prompts, our methodology capitalizes on the knowledge GPT-4o has accumulated during its training, showcasing that it can generate accurate and contextually relevant annotations without direct access to multimodal inputs. Therefore, we propose MELT, a multimodal emotion dataset fully annotated by GPT-4o. We demonstrate the effectiveness of MELT by fine-tuning four self-supervised learning (SSL) backbones and assessing speech emotion recognition performance across emotion datasets. Additionally, our subjective experiments\' results demonstrate a consistence performance improvement on SER.

Related papers

• Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification [56.974545305472304]
  Most datasets for sentiment analysis lack context in which an opinion was expressed, often crucial for emotion understanding, and are mainly limited by a few emotion categories.<n>We design an LLM-based data synthesis pipeline and leverage a large model, Mistral-7b, for the generation of training examples for more accessible, lightweight BERT-type encoder models.<n>We show that Emo Pillars models are highly adaptive to new domains when tuned to specific tasks such as GoEmotions, ISEAR, IEMOCAP, and EmoContext, reaching the SOTA performance on the first three.
  arXiv  Detail & Related papers  (2025-04-23T16:23:17Z)
• Roadmap towards Superhuman Speech Understanding using Large Language Models [60.57947401837938]
  Large language models (LLMs) integrate speech and audio data. Recent advances, such as GPT-4o, highlight the potential for end-to-end speech LLMs. We propose a five-level roadmap, ranging from basic automatic speech recognition (ASR) to advanced superhuman models.
  arXiv  Detail & Related papers  (2024-10-17T06:44:06Z)
• From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs [12.199629860735195]
  We compare GPT4 with supervised models and or humans in three aspects: agreement with human annotations, alignment with human perception, and impact on model training. We find that common metrics that use aggregated human annotations as ground truth can underestimate the performance, of GPT-4.
  arXiv  Detail & Related papers  (2024-08-30T05:50:15Z)
• Textualized and Feature-based Models for Compound Multimodal Emotion Recognition in the Wild [45.29814349246784]
  multimodal large language models (LLMs) rely on explicit non-verbal cues that may be translated from different non-textual modalities into text. This paper compares the potential of text- and feature-based approaches for compound multimodal ER in videos.
  arXiv  Detail & Related papers  (2024-07-17T18:01:25Z)
• Large Language Models Understand Layout [6.732578061359833]
  Large language models (LLMs) demonstrate extraordinary abilities in a wide range of natural language processing (NLP) tasks. We show that, beyond text understanding capability, LLMs are capable of processing text layouts denoted by spatial markers. We show that layout understanding ability is beneficial for building efficient visual question-answering (VQA) systems.
  arXiv  Detail & Related papers  (2024-07-08T09:03:12Z)
• Can LLMs Understand the Implication of Emphasized Sentences in Dialogue? [64.72966061510375]
  Emphasis is a crucial component in human communication, which indicates the speaker's intention and implication beyond pure text in dialogue. This paper introduces Emphasized-Talk, a benchmark with emphasis-annotated dialogue samples capturing the implications of emphasis. We evaluate various Large Language Models (LLMs), both open-source and commercial, to measure their performance in understanding emphasis.
  arXiv  Detail & Related papers  (2024-06-16T20:41:44Z)
• Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models [23.670143829183104]
  The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. We propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information. Our method improves alignment between inputs and their human-annotated labels from both an empirical and human-evaluated standpoint.
  arXiv  Detail & Related papers  (2023-11-06T21:34:49Z)
• TouchStone: Evaluating Vision-Language Models by Language Models [91.69776377214814]
  We propose an evaluation method that uses strong large language models as judges to comprehensively evaluate the various abilities of LVLMs. We construct a comprehensive visual dialogue dataset TouchStone, consisting of open-world images and questions, covering five major categories of abilities and 27 subtasks. We demonstrate that powerful LVLMs, such as GPT-4, can effectively score dialogue quality by leveraging their textual capabilities alone.
  arXiv  Detail & Related papers  (2023-08-31T17:52:04Z)
• Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Loss [80.79641247882012]
  We focus on unsupervised feature learning for Multimodal Emotion Recognition (MER) We consider discrete emotions, and as modalities text, audio and vision are used. Our method, as being based on contrastive loss between pairwise modalities, is the first attempt in MER literature.
  arXiv  Detail & Related papers  (2022-07-23T10:11:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.