Paper Citation Record · LEDGER

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach

As of 10 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 3 inbound Pith citation observations for arXiv:2505.14449.

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

pith.paper-citation-record.v1
2505.14449 v3

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

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measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:41:00.836633Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 092f917b-a03c-4816-b776-61d584088e0b · outbound

This paper cites an unresolved cited work.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Unresolved cited work

Reference 1

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Observation 95a020d3-3c4c-4c29-acc2-f37105825d35 · outbound

This paper cites Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach

Reference 2

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Observation c88ebbce-cd78-4602-b3e9-aff88d2a245b · outbound

This paper cites Central to our approach is the Implicit Demography Inference (IDI) Module, which infers group labels from speech data by combining pseudo-labeling and unsuper- vised clustering.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Central to our approach is the Implicit Demography Inference (IDI) Module, which infers group labels from speech data by combining pseudo-labeling and unsuper- vised clustering

Reference 3

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Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Unresolved cited work

Reference 4

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Observation 28107a93-206f-4b48-abd3-d62caede3fd1 · outbound

This paper cites Young” (20–35 years),“Middle.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Young” (20–35 years),“Middle

Reference 5

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This paper cites First, our experiments are based on the CREMA-D dataset, which comprises acted emotional expressions; thus, the generalizability to naturalistic settings remains to be vali- dated.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach First, our experiments are based on the CREMA-D dataset, which comprises acted emotional expressions; thus, the generalizability to naturalistic settings remains to be vali- dated

Reference 6

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Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Unresolved cited work

Reference 7

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Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Unresolved cited work

Reference 8

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Observation b075f5a8-d7d8-45e8-9ba3-f9d8e7142259 · outbound

This paper cites Prioritizing data acquisition for end-to-end speech model improvement,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Prioritizing data acquisition for end-to-end speech model improvement,

Reference 9

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Observation 7f796efa-ce28-4a13-ab9c-50d83ff1a7c5 · outbound

This paper cites Mitigating bias against non-native accents,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Mitigating bias against non-native accents,

Reference 10

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Observation f9dabed5-3712-4b7f-b401-736a788e9e3e · outbound

This paper cites Speech emotion recognition for hu- man–computer interaction,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Speech emotion recognition for hu- man–computer interaction,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:11.133666Z

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Observation 5f67a00a-0882-4ca5-adfe-bd5474c885d7 · outbound

This paper cites Discrimination, Bias, Fairness, and Trustworthy AI,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Discrimination, Bias, Fairness, and Trustworthy AI,

Reference 12

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Observation da81a1a5-b330-442e-810d-de8505e28dae · outbound

This paper cites Fairness Without Demographic Data: A Survey of Approaches,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Fairness Without Demographic Data: A Survey of Approaches,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:10.740074Z

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Observation 8c43318d-ee9a-4dd8-95b5-3ea18956f14f · outbound

This paper cites Emo- bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Emo- bias: A Large Scale Evaluation of Social Bias on Speech Emotion Recognition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:10.538296Z

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Observation ab348a60-9fcb-40de-a6bd-133bb5f352fe · outbound

This paper cites Gen- der De-Biasing in Speech Emotion Recognition,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Gen- der De-Biasing in Speech Emotion Recognition,

Reference 15

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Observation 09a15afa-bc84-4416-adaf-730fb2886dae · outbound

This paper cites On the social bias of speech self-supervised models,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach On the social bias of speech self-supervised models,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:10.204722Z

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Observation fed7173d-5648-43d6-ace5-5ff08018694f · outbound

This paper cites Achieving Fair Speech Emotion Recognition via Perceptual Fairness,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Achieving Fair Speech Emotion Recognition via Perceptual Fairness,

Reference 17

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Observation 3d803771-6e21-43c7-9dff-8399865ed945 · outbound

This paper cites Balancing Speaker- Rater Fairness for Gender-Neutral Speech Emotion Recognition,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Balancing Speaker- Rater Fairness for Gender-Neutral Speech Emotion Recognition,

Reference 18

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Observation 3cc391ae-0012-4ea9-bdc9-fcc49113b3de · outbound

This paper cites Good practices for evaluation of machine learning systems,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Good practices for evaluation of machine learning systems,

Reference 19

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Observation fd29b79a-ab92-4c31-a5dc-3c3f29f91862 · outbound

This paper cites Speech-based Age and Gender Prediction with Transformers,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Speech-based Age and Gender Prediction with Transformers,

Reference 20

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Observation 184d3cb0-2e59-4534-9ac0-2767ff7f32a5 · outbound

This paper cites The cluster assignments derived from this process are treated as group labels, reflecting latent structures within the data that may correspond to demographic differ- ences.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach The cluster assignments derived from this process are treated as group labels, reflecting latent structures within the data that may correspond to demographic differ- ences

Reference 21

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Observation 6cd4ef12-f657-4c4b-b3c5-4ea8f52bdea9 · outbound

This paper cites Embracing Ambiguity And Subjectivity Using The All-Inclusive Aggregation Rule For Eval- uating Multi-Label Speech Emotion Recognition Systems,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Embracing Ambiguity And Subjectivity Using The All-Inclusive Aggregation Rule For Eval- uating Multi-Label Speech Emotion Recognition Systems,

Reference 22

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This paper cites Open-Emotion: A Reproducible EMO-Superb For Speech Emotion Recognition Systems,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Open-Emotion: A Reproducible EMO-Superb For Speech Emotion Recognition Systems,

Reference 23

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Observation 99a344cc-8965-4907-9745-922e11773b2b · outbound

This paper cites Semantic Space Theory: A Com- putational Approach to Emotion,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Semantic Space Theory: A Com- putational Approach to Emotion,

Reference 24

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Observation c65cf16e-ef30-420c-84ea-e12b920b853c · outbound

This paper cites We use the class- balanced cross-entropy loss [26] as base SER loss LSER.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach We use the class- balanced cross-entropy loss [26] as base SER loss LSER

Reference 25

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This paper cites ECAPA- TDNN: Emphasized Channel Attention, Propagation and Ag- gregation in TDNN Based Speaker Verification,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach ECAPA- TDNN: Emphasized Channel Attention, Propagation and Ag- gregation in TDNN Based Speaker Verification,

Reference 26

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Observation 2194637b-2bd0-4f14-a5d0-f2eb31791f31 · outbound

This paper cites CREMA-D: Crowd-Sourced Emotional Multi- modal Actors Dataset,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach CREMA-D: Crowd-Sourced Emotional Multi- modal Actors Dataset,

Reference 27

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Observation 6436786a-ee6b-4c4c-a4b1-8851c855c248 · outbound

This paper cites Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:09.035892Z

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Observation aab9a88c-4ab2-487a-83e9-f3bb6d0b2fda · outbound

This paper cites Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models,

Reference 29

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Observation d98abf45-6c8f-4ac2-9649-ff28ac1160b9 · outbound

This paper cites Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Stimulus Modality Matters: Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance,

Reference 30

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This paper cites Least squares quantization in PCM,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Least squares quantization in PCM,

Reference 31

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This paper cites Data preprocessing techniques for classification without discrimination,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Data preprocessing techniques for classification without discrimination,

Reference 32

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This paper cites Robust Solutions of Optimization Problems Affected by Uncertain Probabilities,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Robust Solutions of Optimization Problems Affected by Uncertain Probabilities,

Reference 33

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This paper cites Dis- tributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Dis- tributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:07.584784Z

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This paper cites WavLM: Large-Scale Self-Supervised Pre- Training for Full Stack Speech Processing,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach WavLM: Large-Scale Self-Supervised Pre- Training for Full Stack Speech Processing,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:07.407240Z

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This paper cites Class- Balanced Loss Based on Effective Number of Samples,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Class- Balanced Loss Based on Effective Number of Samples,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:07.210152Z

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This paper cites Learning from fail- ure: training debiased classifier from biased classifier,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Learning from fail- ure: training debiased classifier from biased classifier,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:07.010417Z

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Observation 20d912e6-b341-4813-9fcf-0270bf721257 · outbound

This paper cites Learning Debiased Representation via Disentangled Feature Augmentation,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Learning Debiased Representation via Disentangled Feature Augmentation,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:06.819142Z

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Observation 5be0fcc5-e34a-4125-b16c-446d90979e78 · outbound

This paper cites Distributionally Robust Optimization with Probabilistic Group,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Distributionally Robust Optimization with Probabilistic Group,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:06.586105Z

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Observation 7dc8654a-179e-4bd1-b934-717f18dba598 · outbound

This paper cites Diverse adversaries for miti- gating bias in training,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Diverse adversaries for miti- gating bias in training,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:06.383619Z

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Observation 81fef448-bc92-46bd-88b8-66087a10a805 · outbound

This paper cites Bias in bios: A case study of semantic representation bias in a high- stakes setting,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Bias in bios: A case study of semantic representation bias in a high- stakes setting,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:06.191209Z

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Observation 1d633539-a05a-4ede-9f9e-a2f30352e175 · outbound

This paper cites Visualizing Data using t-SNE,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Visualizing Data using t-SNE,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:06.037642Z

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Observation e10a933c-6e09-423a-bb44-c334bafa7297 · outbound

This paper cites Jointly Learning From Unimodal and Multimodal- Rated Labels in Audio-Visual Emotion Recognition,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Jointly Learning From Unimodal and Multimodal- Rated Labels in Audio-Visual Emotion Recognition,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:05.822884Z

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Observation c0b22830-320a-4f7b-b4d8-872eba27a3d3 · outbound

This paper cites Listen and speak fairly: a study on semantic gender bias in speech integrated large language models,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Listen and speak fairly: a study on semantic gender bias in speech integrated large language models,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:41:05.692926Z

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Observation 0ea601e7-317c-4786-aca3-73ec375a257c · outbound

This paper cites Improving speech emotion recognition in under-resourced languages via speech-to-speech translation with bootstrapping data selection,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Improving speech emotion recognition in under-resourced languages via speech-to-speech translation with bootstrapping data selection,

Reference 45

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Observation 4de754cf-7da8-4108-816a-a35714c1abfe · outbound

This paper cites Don’t speak too fast: The impact of data bias on self-supervised speech models,.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Don’t speak too fast: The impact of data bias on self-supervised speech models,

Reference 47

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Observation e98183e2-2730-4218-b84c-5c6f597722f4 · outbound

This paper cites Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection.

Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection

Reference 2025

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Pith citing papers

Observation 95a020d3-3c4c-4c29-acc2-f37105825d35 · inbound