Paper Citation Record · LEDGER

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild

As of 22 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2606.16731.

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

pith.paper-citation-record.v1
2606.16731 v2

Coverage vector

measured 55 of 55 reference resolution

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measured 56 of 56 standing notices

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T03:07:21.595291Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T18:28:48.556997Z

Reference resolution

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Outbound references

Observation f36efb2e-6f6f-4bd5-92ce-69391bc00b89 · outbound

This paper cites MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild

Reference 1

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Observation c76e66d1-cb7e-4d53-99ca-9f1b701a0873 · outbound

This paper cites Active Speaker Detection vs.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Active Speaker Detection vs

Reference 2

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Observation 3139902c-37a5-47ac-b9f9-00f30bcda4ff · outbound

This paper cites Speakers use these visual sig- nals alongside prosodic contours to navigate turn-taking [25].

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Speakers use these visual sig- nals alongside prosodic contours to navigate turn-taking [25]

Reference 3

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Observation e0ee3743-5aaa-456e-8a35-ab48ed0eadc4 · outbound

This paper cites As illustrated in Figure 2, these sources contain editing artifacts like jump cuts that disrupt the temporal flow of conversation.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild As illustrated in Figure 2, these sources contain editing artifacts like jump cuts that disrupt the temporal flow of conversation

Reference 4

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Observation bb8d106b-18b1-4eac-9513-9608b5378572 · outbound

This paper cites cur- rent floor holder.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild cur- rent floor holder

Reference 5

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Observation f66b50f1-5110-4f3b-bcbf-6c197d1de354 · outbound

This paper cites The speaker with the highest activity is designated as the current/past floor holder.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild The speaker with the highest activity is designated as the current/past floor holder

Reference 6

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Observation 7f8598bf-38c4-4d92-a7ff-974d3538f7ed · outbound

This paper cites social conductor.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild social conductor

Reference 7

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Observation 8e47ae96-bca5-491c-a25a-0a9335b08040 · outbound

This paper cites Each dataset is mapped to specific mod- ules to facilitate multi-stage training as shown in Table 1.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Each dataset is mapped to specific mod- ules to facilitate multi-stage training as shown in Table 1

Reference 8

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Observation 92989b43-558d-415a-bf03-49d5950ed0b0 · outbound

This paper cites To measure the usefulness of these learned embeddings, we de- fine a set of downstream tasks for evaluation.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild To measure the usefulness of these learned embeddings, we de- fine a set of downstream tasks for evaluation

Reference 9

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Observation d9d06664-d428-496f-b7d6-66f34eea6575 · outbound

This paper cites Results are reported as mean ± 95% confidence intervals over ten runs using random seeds 42–51.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Results are reported as mean ± 95% confidence intervals over ten runs using random seeds 42–51

Reference 10

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Observation 8c73ec7f-3f6e-4d12-bdad-38dc3f72a73a · outbound

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MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Unresolved cited work

Reference 11

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Observation 75617a5c-7956-4b5f-8242-c9c0d56c36f6 · outbound

This paper cites To address the scaling bottlenecks of joint modeling, we proposed a Role- Relative Projection that compresses complex group dynamics into a scalable pairwise state.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild To address the scaling bottlenecks of joint modeling, we proposed a Role- Relative Projection that compresses complex group dynamics into a scalable pairwise state

Reference 12

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This paper cites The computations and data handling were enabled by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild The computations and data handling were enabled by the Berzelius resource provided by the Knut and Alice Wallenberg Foundation at the National Supercomputer Centre

Reference 13

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This paper cites These tools were not used for writing any major parts of the paper.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild These tools were not used for writing any major parts of the paper

Reference 14

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Observation 4ce2c715-9606-4595-8329-3eb480042423 · outbound

This paper cites Some signals and rules for taking speaking turns in conversations,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Some signals and rules for taking speaking turns in conversations,

Reference 15

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Observation b4e5f3ff-1ae7-42eb-a55b-7b1ec722468f · outbound

This paper cites A simplest system- atics for the organization of turn-taking for conversation,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild A simplest system- atics for the organization of turn-taking for conversation,

Reference 16

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Observation 6cf5e31e-fdc8-4731-99f1-c01cc91240ff · outbound

This paper cites On the structure of speaker–auditor interaction dur- ing speaking turns,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild On the structure of speaker–auditor interaction dur- ing speaking turns,

Reference 17

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Observation e631b712-18cf-470f-9b4e-dbcedcb34f2d · outbound

This paper cites Interactional units in conver- sation: Syntactic, intonational, and pragmatic resources for the management of turns,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Interactional units in conver- sation: Syntactic, intonational, and pragmatic resources for the management of turns,

Reference 18

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Observation 139550a6-2339-4df4-a36a-5305bd8f8db9 · outbound

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MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Unresolved cited work

Reference 19

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This paper cites Listeners’ responses to filled pauses in relation to floor apportionment,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Listeners’ responses to filled pauses in relation to floor apportionment,

Reference 20

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MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Duncan and D

Reference 21

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Observation 263b65d1-e839-4157-be7b-a8b5c34d0267 · outbound

This paper cites Universals and cultural variation in turn-taking in conversation,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Universals and cultural variation in turn-taking in conversation,

Reference 22

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Observation d9564aa5-84a4-47fd-8cd2-501c8ab63b54 · outbound

This paper cites Timing in turn-taking and its im- plications for processing models of language,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Timing in turn-taking and its im- plications for processing models of language,

Reference 23

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Observation 576ab0ff-5dec-4e55-a7d3-7bd74f544ffc · outbound

This paper cites Conversational interaction with social robots,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Conversational interaction with social robots,

Reference 24

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Observation b36c8ee4-702c-4379-b974-53d4b1373a07 · outbound

This paper cites V oice Activity Projection: Self- supervised Learning of Turn-taking Events,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild V oice Activity Projection: Self- supervised Learning of Turn-taking Events,

Reference 25

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This paper cites Yeah, un, oh: Continuous and real-time backchannel prediction with fine-tuning of voice activity projection,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Yeah, un, oh: Continuous and real-time backchannel prediction with fine-tuning of voice activity projection,

Reference 26

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Observation 29182a9b-607a-47a4-af56-173a9ef98ff2 · outbound

This paper cites Applying general turn-taking models to conversational human-robot interaction,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Applying general turn-taking models to conversational human-robot interaction,

Reference 27

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Observation e4670881-626e-4cac-b08f-8c61af0a82d9 · outbound

This paper cites Multimodal turn analysis and prediction for multi-party conversations,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Multimodal turn analysis and prediction for multi-party conversations,

Reference 28

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Observation 230c954f-88e0-4382-b50b-abf875c123de · outbound

This paper cites Triadic multi- party voice activity projection for turn-taking in spoken dialogue systems,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Triadic multi- party voice activity projection for turn-taking in spoken dialogue systems,

Reference 29

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This paper cites MSDWild: Multi- modal Speaker Diarization Dataset in the Wild,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild MSDWild: Multi- modal Speaker Diarization Dataset in the Wild,

Reference 30

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Observation 39a7fd18-d883-4e6d-aa79-84f9d8f692d9 · outbound

This paper cites Is someone speaking? exploring long-term temporal features for audio-visual active speaker detection,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Is someone speaking? exploring long-term temporal features for audio-visual active speaker detection,

Reference 31

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This paper cites Loconet: Long-short con- text network for active speaker detection,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Loconet: Long-short con- text network for active speaker detection,

Reference 32

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This paper cites A V A Active Speaker: An audio-visual dataset for active speaker detection,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild A V A Active Speaker: An audio-visual dataset for active speaker detection,

Reference 33

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Observation 67e01810-8c63-42cd-b057-5e4c1ccbacfb · outbound

This paper cites How much does prosody help turn- taking? Investigations using voice activity projection models,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild How much does prosody help turn- taking? Investigations using voice activity projection models,

Reference 34

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Observation f75a2a91-6178-4897-8442-a3a11e608d87 · outbound

This paper cites Pre- dicting next speaker and timing from gaze transition patterns in multi-party meetings,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Pre- dicting next speaker and timing from gaze transition patterns in multi-party meetings,

Reference 35

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This paper cites Gaze-enhanced multimodal turn-taking prediction in triadic conversations,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Gaze-enhanced multimodal turn-taking prediction in triadic conversations,

Reference 36

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Observation 55edde12-ffb7-4a86-9a28-e4bfcdf57877 · outbound

This paper cites Multimodal voice activ- ity prediction: Turn-taking events detection in expert-novice con- versation,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Multimodal voice activ- ity prediction: Turn-taking events detection in expert-novice con- versation,

Reference 37

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This paper cites Prediction of who will be next speaker and when using mouth- opening pattern in multi-party conversation,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Prediction of who will be next speaker and when using mouth- opening pattern in multi-party conversation,

Reference 38

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MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Who’s next? speaker-selection mech- anisms in multiparty dialogue,

Reference 39

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This paper cites A computational study on sentence-based next speaker prediction in multiparty conversa- tions,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild A computational study on sentence-based next speaker prediction in multiparty conversa- tions,

Reference 40

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This paper cites Multimodal continuous turn-taking prediction using multiscale rnns,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Multimodal continuous turn-taking prediction using multiscale rnns,

Reference 41

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This paper cites Visual cues enhance predictive turn- taking for two-party human interaction,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Visual cues enhance predictive turn- taking for two-party human interaction,

Reference 42

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This paper cites Multi-channel sequence-to-sequence neural diarization: Experimental results for the misp 2025 challenge,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Multi-channel sequence-to-sequence neural diarization: Experimental results for the misp 2025 challenge,

Reference 43

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This paper cites Enhancing gaze prediction in multi- party conversations via speaker-aware multimodal adaptation,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Enhancing gaze prediction in multi- party conversations via speaker-aware multimodal adaptation,

Reference 44

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This paper cites How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild How to Design a Three-Stage Architecture for Audio-Visual Active Speaker Detection in the Wild

Reference 45

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This paper cites Ego4d: Around the world in 3,000 hours of egocentric video,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Ego4d: Around the world in 3,000 hours of egocentric video,

Reference 46

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This paper cites The ami meeting corpus,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild The ami meeting corpus,

Reference 47

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This paper cites Masked face recognition challenge: The insightface track report,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Masked face recognition challenge: The insightface track report,

Reference 48

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MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Reti- naface: Single-shot multi-level face localisation in the wild,

Reference 49

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MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Sample and Computation Redistribution for Efficient Face Detection

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-03T18:28:48.555627Z

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This paper cites Arcface: Additive an- gular margin loss for deep face recognition,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Arcface: Additive an- gular margin loss for deep face recognition,

Reference 51

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This paper cites The hungarian method for the assignment prob- lem,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild The hungarian method for the assignment prob- lem,

Reference 52

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This paper cites Attention is all you need,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Attention is all you need,

Reference 53

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This paper cites Talknce: Improving active speaker detection with talk- aware contrastive learning,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Talknce: Improving active speaker detection with talk- aware contrastive learning,

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This paper cites Turn-taking in conversational systems and human- robot interaction: A review,.

MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild Turn-taking in conversational systems and human- robot interaction: A review,

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