Paper Citation Record · LEDGER

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2506.13971.

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

pith.paper-citation-record.v1
2506.13971 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:02:34.050123Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:02:31.400913Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:02:34.268817Z

Reference resolution

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 14f71907-93c9-4273-8391-f6dec1e4d4e9 · outbound

This paper cites Although it is an es- sential medium for communication, it has not been sufficiently studied.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Although it is an es- sential medium for communication, it has not been sufficiently studied

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.913476Z

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Observation 5beace89-127d-4171-87da-9ce7ded35caf · outbound

This paper cites While audio-based SSL has been widely applied in speech emotion recognition, multimodal approaches have only recently emerged [5].

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience While audio-based SSL has been widely applied in speech emotion recognition, multimodal approaches have only recently emerged [5]

Reference 2

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Observation 250380de-4773-4642-becc-ff26f686d791 · outbound

This paper cites Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience

Reference 3

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Observation 9f3f8ed2-9ff6-4fb8-954c-66f9a1ebedc8 · outbound

This paper cites Data split To analyze the impact of the labeled data ratio on SSL model performance, we partitioned the targeted clips data into 10 folds.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Data split To analyze the impact of the labeled data ratio on SSL model performance, we partitioned the targeted clips data into 10 folds

Reference 4

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Observation 3721b4df-c3fd-44c2-b52a-b7f439701f61 · outbound

This paper cites They both outperformed SL counterparts at nearly every levels of labeled data in both ROC-AUC and macro F1 score by 1-4% for predicting Enjoy- ment or Fluidity.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience They both outperformed SL counterparts at nearly every levels of labeled data in both ROC-AUC and macro F1 score by 1-4% for predicting Enjoy- ment or Fluidity

Reference 5

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This paper cites We also demonstrated this approach gener- alizes to new sessions with different participants.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience We also demonstrated this approach gener- alizes to new sessions with different participants

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.883948Z

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Observation 0f6851fd-ef72-4fd4-9693-06c0175db9c6 · outbound

This paper cites are supported by NYU Discovery Research Fund for Human Health.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience are supported by NYU Discovery Research Fund for Human Health

Reference 7

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Observation 0216becc-1531-4bb6-a341-b865ff7dd3d3 · outbound

This paper cites Sepa- rable processes for live “in-person.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Sepa- rable processes for live “in-person

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.869522Z

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Observation d6a1ce21-fb37-41f4-b618-189606ad5a20 · outbound

This paper cites Perceiving others through a screen: Are first im- pressions of personality accurate and normative via videocon- ferencing?.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Perceiving others through a screen: Are first im- pressions of personality accurate and normative via videocon- ferencing?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.862748Z

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Observation 9fb2e6b3-aa66-43f7-9561-daa65b84c27b · outbound

This paper cites Virtual (zoom) interactions alter conversational behavior and in- terbrain coherence,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Virtual (zoom) interactions alter conversational behavior and in- terbrain coherence,

Reference 10

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Observation 7c8af8da-14d1-4bb1-8472-e1264d55b435 · outbound

This paper cites The effect of video feedback delay on frustration and emo- tion communication accuracy,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience The effect of video feedback delay on frustration and emo- tion communication accuracy,

Reference 11

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Observation 4d884b55-e1ab-4fe5-b2cf-73be7dc8d62f · outbound

This paper cites A survey on the semi supervised learning paradigm in the context of speech emotion recognition,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience A survey on the semi supervised learning paradigm in the context of speech emotion recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.839435Z

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Observation 0d0b9bfc-2c06-456a-9dc8-ab550a3f792f · outbound

This paper cites Combining cross-modal knowledge transfer and semi-supervised learning for speech emotion recognition,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Combining cross-modal knowledge transfer and semi-supervised learning for speech emotion recognition,

Reference 13

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Observation 712e1a3e-f380-40fa-8c8c-a310fe7cc07e · outbound

This paper cites SMIN: Semi-supervised multi- modal interaction network for conversational emotion recogni- tion,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience SMIN: Semi-supervised multi- modal interaction network for conversational emotion recogni- tion,

Reference 14

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Observation 24621e28-0fef-4fdc-8502-08ac0926270d · outbound

This paper cites Multimodal emotion recognition with vision-language prompting and modality dropout,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Multimodal emotion recognition with vision-language prompting and modality dropout,

Reference 15

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Observation a8a013e0-c416-412d-be93-08bf6fd8f456 · outbound

This paper cites Focused or stuck together: multimodal patterns reveal triads’ performance in collaborative problem solving,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Focused or stuck together: multimodal patterns reveal triads’ performance in collaborative problem solving,

Reference 16

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Observation fb84d95b-bce3-4858-8a4c-18a3ddeda976 · outbound

This paper cites QoE estimation of webRTC-based audio-visual conversations from facial and speech features,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience QoE estimation of webRTC-based audio-visual conversations from facial and speech features,

Reference 17

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Observation a4041642-c90e-4244-a9e2-07fbc52f954b · outbound

This paper cites Multimodal machine learning can predict videoconference fluidity and enjoyment,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Multimodal machine learning can predict videoconference fluidity and enjoyment,

Reference 18

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Observation bced800f-af80-4f3d-98e8-698201cade0c · outbound

This paper cites A survey on semi-supervised learning,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience A survey on semi-supervised learning,

Reference 19

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Observation 864c3047-534a-4915-a15b-57963d720772 · outbound

This paper cites Self-training: A survey,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Self-training: A survey,

Reference 20

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Observation 8222a713-f43e-4f7e-b14a-22b717b03ba1 · outbound

This paper cites RoomReader: A multimodal corpus of online multiparty conversational interactions,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience RoomReader: A multimodal corpus of online multiparty conversational interactions,

Reference 21

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Observation 13a6602f-8406-481c-89c1-fdc64f4a56af · outbound

This paper cites Zoom disrupts the rhythm of conversation.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Zoom disrupts the rhythm of conversation

Reference 22

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This paper cites CNN architectures for large-scale audio classification,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience CNN architectures for large-scale audio classification,

Reference 23

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This paper cites Openface 2.0: Facial behavior analysis toolkit,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Openface 2.0: Facial behavior analysis toolkit,

Reference 24

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Observation aa76d053-f642-4314-a29a-1d5bb0f78f73 · outbound

This paper cites Sentence-BERT: Sentence embeddings using siamese BERT-networks,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Sentence-BERT: Sentence embeddings using siamese BERT-networks,

Reference 25

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Observation 5207f3bf-45a3-4738-850c-1ca9d3ce2efb · outbound

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

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Dawn of the trans- former era in speech emotion recognition: closing the valence gap,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.561797Z

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This paper cites Unsupervised word sense disambiguation rivaling supervised methods,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Unsupervised word sense disambiguation rivaling supervised methods,

Reference 27

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Observation 03005f86-1240-4a87-b6d5-980d033a85a2 · outbound

This paper cites A new analysis of co-training.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience A new analysis of co-training

Reference 28

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Observation 6f9e520e-f388-4c9d-8b4d-c8f99b3b2357 · outbound

This paper cites SSLearn: A semi-supervised learning li- brary for python,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience SSLearn: A semi-supervised learning li- brary for python,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.736454Z

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This paper cites Optuna: A next-generation hyperparameter optimization framework,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Optuna: A next-generation hyperparameter optimization framework,

Reference 30

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Observation c661bc39-f3d8-407d-8117-f1ed423aa00d · outbound

This paper cites 3m- transformer: A multi-stage multi-stream multimodal transformer for embodied turn-taking prediction,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience 3m- transformer: A multi-stage multi-stream multimodal transformer for embodied turn-taking prediction,

Reference 31

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Observation 6cb51c3a-63e5-4a15-b7cb-7dc0c3f0187f · outbound

This paper cites Pre- dicting conversation outcomes using multimodal transformer,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Pre- dicting conversation outcomes using multimodal transformer,

Reference 32

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Observation fe291e78-ff26-45ad-a4fc-7b6ce2ea9c1e · outbound

This paper cites Dyadformer: A multi-modal transformer for long-range model- ing of dyadic interactions,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Dyadformer: A multi-modal transformer for long-range model- ing of dyadic interactions,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.708267Z

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Observation 18b0d01b-1508-4a5d-b42d-74d9253f400c · outbound

This paper cites CTNet: Conversational transformer network for emotion recognition,.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience CTNet: Conversational transformer network for emotion recognition,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:02:34.477575Z

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Observation 70a47fcc-fc55-42ad-a552-f1b3cfd54226 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Multimodal Fusion with Semi-Supervised Learning Minimizes Annotation Quantity for Modeling Videoconference Conversation Experience Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 2019

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Pith citing papers

Observation 250380de-4773-4642-becc-ff26f686d791 · inbound