Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2505.24493.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.24493 v1

Coverage vector

measured 39 of 39 reference resolution

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measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:27:26.864720Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T12:27:31.120281Z

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 402ac5de-66b2-4199-bf35-69f4371c4609 · outbound

This paper cites MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge.

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

Reference 1

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Observation 0a6528c0-cd32-4f50-9cfd-fd596d54520c · outbound

This paper cites 1st Customer.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge 1st Customer

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:34.805142Z

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Observation 633642c8-a18f-4dbf-bec1-c711c7d9406c · outbound

This paper cites Emo Prediction.

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

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:34.608199Z

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Observation 26ad3144-fb9d-4890-a481-7c9e6c2b4a38 · outbound

This paper cites Subjective Experiment To assess the emotion annotation quality, we invited 20 par- ticipants, comprising 11 males and 9 females to conduct a Mean Opinion Score (MOS) experiment.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Subjective Experiment To assess the emotion annotation quality, we invited 20 par- ticipants, comprising 11 males and 9 females to conduct a Mean Opinion Score (MOS) experiment

Reference 4

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Observation 77a5abfc-b9ba-4998-a0a1-536b1eb404c4 · outbound

This paper cites Performance The overall MOS result is shown in Fig.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Performance The overall MOS result is shown in Fig

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:34.513190Z

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Observation 2870d4d2-c87c-4e41-b056-05e6767a9f00 · outbound

This paper cites To achieve this, we developed a prompting strategy incorporating cross-validation and CoT rea- soning to ensure consistent and accurate annotations.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge To achieve this, we developed a prompting strategy incorporating cross-validation and CoT rea- soning to ensure consistent and accurate annotations

Reference 6

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Observation 40bc5abf-dbd0-4a94-9992-2aad67ef0218 · outbound

This paper cites Schuller is also with the Munich Data Science Insti- tute and the Konrad Zuse School of Excellence in Reliable AI, both in Munich, Germany.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Schuller is also with the Munich Data Science Insti- tute and the Konrad Zuse School of Excellence in Reliable AI, both in Munich, Germany

Reference 7

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Observation 67387979-9e87-4696-b7c9-e968f89b0446 · outbound

This paper cites Be- yond deep learning: Charting the next frontiers of affective com- puting,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Be- yond deep learning: Charting the next frontiers of affective com- puting,

Reference 8

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Observation 3f2e4355-2c90-4e2f-b94c-a6a4d105c234 · outbound

This paper cites En- hancing emotional text-to-speech controllability with natural lan- guage guidance through contrastive learning and diffusion mod- els,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge En- hancing emotional text-to-speech controllability with natural lan- guage guidance through contrastive learning and diffusion mod- els,

Reference 9

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Observation c5e9f569-d767-4100-bfc5-57040ed1b31d · outbound

This paper cites Emotion recognition in context,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Emotion recognition in context,

Reference 10

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This paper cites Contextual Emotion Recognition using Large Vision Language Models.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Contextual Emotion Recognition using Large Vision Language Models

Reference 11

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This paper cites The human in emotion recognition on social media: Attitudes, outcomes, risks,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge The human in emotion recognition on social media: Attitudes, outcomes, risks,

Reference 12

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Observation e6423b5b-6bd8-4dc8-96b2-940c3cc04980 · outbound

This paper cites Language models are unsupervised multitask learners,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Language models are unsupervised multitask learners,

Reference 13

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Observation c4c1fadf-d6eb-4ee0-b898-87f0b163e5a7 · outbound

This paper cites Language Models are Few-Shot Learners.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Language Models are Few-Shot Learners

Reference 14

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Observation 507770af-6dac-4c0d-b692-82ef45a2283f · outbound

This paper cites GPT-4o System Card.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge GPT-4o System Card

Reference 15

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Observation 24e35159-b0a7-4c28-9c50-f89fddd93f71 · outbound

This paper cites Large Language Models for Data Annotation and Synthesis: A Survey.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Large Language Models for Data Annotation and Synthesis: A Survey

Reference 16

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Observation 5b00d479-fa07-4bc1-8d12-dd421f3e78cf · outbound

This paper cites Chatgpt vs. human annotators: A comprehen- sive analysis of chatgpt for text annotation,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Chatgpt vs. human annotators: A comprehen- sive analysis of chatgpt for text annotation,

Reference 17

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This paper cites Chatgpt outperforms crowd workers for text-annotation tasks,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Chatgpt outperforms crowd workers for text-annotation tasks,

Reference 18

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Observation bc585383-0a91-4d08-9cc7-15eb89bb6556 · outbound

This paper cites Wavcaps: A chatgpt-assisted weakly- labelled audio captioning dataset for audio-language multimodal research,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Wavcaps: A chatgpt-assisted weakly- labelled audio captioning dataset for audio-language multimodal research,

Reference 19

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Observation a3d040bf-46f1-4e6d-bd62-7a2bc5540e12 · outbound

This paper cites Pengi: An audio language model for audio tasks,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Pengi: An audio language model for audio tasks,

Reference 20

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This paper cites Secap: Speech emotion captioning with large language model,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Secap: Speech emotion captioning with large language model,

Reference 21

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Observation 829657dd-f225-475a-a7ac-269940d2f011 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge LLaMA: Open and Efficient Foundation Language Models

Reference 22

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This paper cites Meld: A multimodal multi-party dataset for emo- tion recognition in conversations,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Meld: A multimodal multi-party dataset for emo- tion recognition in conversations,

Reference 23

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This paper cites On the time course of vocal emotion recognition,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge On the time course of vocal emotion recognition,

Reference 24

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This paper cites Applying tdnn architectures for an- alyzing duration dependencies on speech emotion recognition.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Applying tdnn architectures for an- alyzing duration dependencies on speech emotion recognition

Reference 25

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This paper cites A wide evaluation of chatgpt on affective computing tasks,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge A wide evaluation of chatgpt on affective computing tasks,

Reference 26

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Observation 8a311e23-6e8a-4094-aeec-2a6c6432be05 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 27

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This paper cites Dawn of the trans- former era in speech emotion recognition: closing the valence gap,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Dawn of the trans- former era in speech emotion recognition: closing the valence gap,

Reference 28

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This paper cites Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,

Reference 29

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This paper cites Wavlm: Large-scale self- supervised pre-training for full stack speech processing,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Wavlm: Large-scale self- supervised pre-training for full stack speech processing,

Reference 30

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This paper cites Iemocap: Interactive emotional dyadic motion capture database,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Iemocap: Interactive emotional dyadic motion capture database,

Reference 31

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Observation 77085dc2-383e-455c-b349-9bcb8a4a2a87 · outbound

This paper cites Toronto emotional speech set (tess).

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Toronto emotional speech set (tess)

Reference 32

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This paper cites The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,

Reference 33

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This paper cites Crema-d: Crowd-sourced emotional multimodal actors dataset,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Crema-d: Crowd-sourced emotional multimodal actors dataset,

Reference 34

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Observation c35b71e7-64cb-4d7c-9fe7-cb23a372826d · outbound

This paper cites EMO-SUPERB: An In-depth Look at Speech Emotion Recognition.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge EMO-SUPERB: An In-depth Look at Speech Emotion Recognition

Reference 35

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This paper cites Paraclap– towards a general language-audio model for computational par- alinguistic tasks,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge Paraclap– towards a general language-audio model for computational par- alinguistic tasks,

Reference 36

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Observation a0867d04-8d09-4afa-88d6-ba04896c76f8 · outbound

This paper cites The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing,

Reference 37

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This paper cites openSMILE: the Munich versatile and fast open-source audio feature extractor,.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge openSMILE: the Munich versatile and fast open-source audio feature extractor,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:27:31.684716Z

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Observation d99fd321-fc6d-4b52-aa62-e1f19f317c61 · outbound

This paper cites How do we describe other people from voices and faces?.

MELT: Towards Automated Multimodal Emotion Data Annotation by Leveraging LLM Embedded Knowledge How do we describe other people from voices and faces?

Reference 39

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Pith citing papers

Observation 402ac5de-66b2-4199-bf35-69f4371c4609 · inbound