Paper Citation Record · LEDGER

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives

As of 13 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.03744.

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

pith.paper-citation-record.v1
2607.03744 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T00:15:26.685663Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b49a4297-6a62-4c34-9000-a403372702a6 · outbound

This paper cites Depression and other common mental disorders: Global health estimates,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Depression and other common mental disorders: Global health estimates,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Unavailable: canonical work link unavailable.

Observation 8ba672af-c612-48d4-9b6b-7577b6475eb7 · outbound

This paper cites A VEC 2013: The continuous audio/visual emotion and depression recognition challenge,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives A VEC 2013: The continuous audio/visual emotion and depression recognition challenge,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation f6ecac02-cea2-4d0b-ab54-844a8203b17d · outbound

This paper cites A VEC 2016: Depression, mood, and emotion recognition workshop and chal- lenge,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives A VEC 2016: Depression, mood, and emotion recognition workshop and chal- lenge,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Unavailable: canonical work link unavailable.

Observation 4a91d901-ec48-49ae-b4ab-2cec703260a6 · outbound

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

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives WavLM: Large-scale self-supervised pre- training for full stack speech processing,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 271d7880-5895-4818-894e-7b5689ccd3ca · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Unavailable: canonical work link unavailable.

Observation 2f2107e4-82a8-4531-9bbc-190acb98b0ea · outbound

This paper cites Using speech recognition technology to investigate the association between timing- related speech features and depression severity,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Using speech recognition technology to investigate the association between timing- related speech features and depression severity,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 428b58ba-d228-4bd9-9437-aa5703845298 · outbound

This paper cites V ocal acoustic biomarkers of depression severity and treatment response,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives V ocal acoustic biomarkers of depression severity and treatment response,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Unavailable: canonical work link unavailable.

Observation 2d0c016d-e150-4bc3-9eb1-9f81ebab6b9b · outbound

This paper cites A review of depression and suicide risk assessment using speech analysis,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives A review of depression and suicide risk assessment using speech analysis,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Unavailable: canonical work link unavailable.

Observation d8714fe7-9156-40dd-8c21-1f2915af9b70 · outbound

This paper cites Identifying mood episodes using dialogue features from clinical interviews,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Identifying mood episodes using dialogue features from clinical interviews,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

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Observation d5737d20-e946-489e-a012-798c210c20cd · outbound

This paper cites Beyond short-frame acoustic features: Capturing long-term speech patterns for depression detection,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Beyond short-frame acoustic features: Capturing long-term speech patterns for depression detection,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

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Observation 799f601b-ec3f-44c2-894d-7c03a2e9c02e · outbound

This paper cites Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 1c9b788f-ca3b-464c-a3e3-c24dd5d60b33 · outbound

This paper cites When LLMs meets acoustic landmarks: An efficient approach to integrate speech into large language models for depression detection,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives When LLMs meets acoustic landmarks: An efficient approach to integrate speech into large language models for depression detection,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 3123cd1f-a937-4e75-961d-b0c537764271 · outbound

This paper cites Joint learning of conversational temporal dynamics and acoustic features for speech deception detection in dialog games,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Joint learning of conversational temporal dynamics and acoustic features for speech deception detection in dialog games,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

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Observation 04fc7b94-fef0-4d44-bed6-c1dbdc851b5e · outbound

This paper cites Automatic deception detec- tion using multiple speech and language communicative descriptors in dialogs,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Automatic deception detec- tion using multiple speech and language communicative descriptors in dialogs,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c51d7163-f1a1-4b76-8b64-70a70d5378b9 · outbound

This paper cites SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation a1e39b70-50fa-4ccd-ab27-c1b5cfac66b5 · outbound

This paper cites The distress analysis interview corpus of human and computer interviews,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives The distress analysis interview corpus of human and computer interviews,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 90f420d2-06d6-4be6-88f8-93670482550d · outbound

This paper cites Detecting depression with audio/text sequence modeling of interviews,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Detecting depression with audio/text sequence modeling of interviews,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f9d317a-570f-4b6a-8f92-9f6390162062 · outbound

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

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1edd64ee-7fc0-4563-b534-deaca4a552e2 · outbound

This paper cites SUPERB: Speech processing universal PERformance benchmark,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives SUPERB: Speech processing universal PERformance benchmark,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

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Observation b7ea9bf5-b56d-444a-b0d0-146a834db87d · outbound

This paper cites Topic modeling based multi-modal depression detection,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Topic modeling based multi-modal depression detection,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc05bffb-9776-4af4-a30a-f059fcb55d51 · outbound

This paper cites Automatic depression detection: An emotional audio-textual corpus and a GRU/BiLSTM-based model,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Automatic depression detection: An emotional audio-textual corpus and a GRU/BiLSTM-based model,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2252212-af93-47ba-8927-4b0a6702c70d · outbound

This paper cites Self-supervised representations in speech-based depression detection,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Self-supervised representations in speech-based depression detection,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation c15ca2f6-f107-428f-9e7b-563df2cc917f · outbound

This paper cites Hierarchical self-supervised repre- sentation learning for depression detection from speech,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Hierarchical self-supervised repre- sentation learning for depression detection from speech,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 9b3ec7a6-6608-4a69-bec5-1beeaab0c52c · outbound

This paper cites Test-time training for speech-based depression detection,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Test-time training for speech-based depression detection,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation b0c2a76f-9045-4353-bd88-c914ef637211 · outbound

This paper cites Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 08521fb0-6c5a-4cdd-8bbf-028aaa28337b · outbound

This paper cites The PHQ-8 as a measure of current depression in the general population,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives The PHQ-8 as a measure of current depression in the general population,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 58963bc1-bb21-4273-8ce2-566bbb4374e1 · outbound

This paper cites Common pitfalls and recommendations for use of machine learning in depression severity estimation: DAIC-WOZ study,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Common pitfalls and recommendations for use of machine learning in depression severity estimation: DAIC-WOZ study,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation fa9f27cb-1e76-409d-871d-6718714436b7 · outbound

This paper cites Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification on the DAIC-WOZ.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Integrating Large Language Models into a Tri-Modal Architecture for Automated Depression Classification on the DAIC-WOZ

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 273b79c2-80a9-4b87-acf6-e4ecb0b63ddd · outbound

This paper cites DAIC-WOZ: On the validity of using the therapist’s prompts in automatic depression detection from clinical interviews,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives DAIC-WOZ: On the validity of using the therapist’s prompts in automatic depression detection from clinical interviews,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation e62d01b3-7de5-45bd-adb0-608a699b742c · outbound

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

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives The geneva minimalistic acoustic parameter set (eGeMAPS) for voice research and affective computing,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Observation 02713e80-67c5-48e9-b60f-a28353a86b6e · outbound

This paper cites Confidence intervals for evaluation in ma- chine learning,.

Can Conversational Temporal Dynamics Improve Depression Detection in Dyads? A Preliminary Investigation in Multi-Modality Perspectives Confidence intervals for evaluation in ma- chine learning,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-12T00:15:26.685663Z

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Pith citing papers

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