Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:16.934337Z
Paper Citation Record · LEDGER
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices
As of 14 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2505.22133.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:20:13.864882Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T13:20:17.225644Z
Reference resolution
29 of 29 outbound references displayed
- verified exact0
- verified fuzzy13
- unresolved15
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 493168fe-9f99-4068-8569-e4cbd5158ad2 · outbound
This paper cites an unresolved cited work.
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T13:20:21.245397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation b16ed2a7-d8e8-4560-942a-bddb759040c3 · outbound
This paper cites Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-07T13:20:17.324877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 10791206-7661-4d21-9cee-ea29b34949b7 · outbound
This paper cites Dataset The IS2025 Emotion Recognition Challenge used the MSP- Podcast dataset v1.12 [19, 10].
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Dataset The IS2025 Emotion Recognition Challenge used the MSP- Podcast dataset v1.12 [19, 10]
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:20.976882Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 142f866e-fd6c-44e6-bf6a-7e3a5e1af53d · outbound
This paper cites Do Speech Foundation Models Impact SER? As suggested by [14], we first investigate whether the choice of speech foundation models impacts the SER performance.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Do Speech Foundation Models Impact SER? As suggested by [14], we first investigate whether the choice of speech foundation models impacts the SER performance
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:20.760307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 172c4eaf-19d5-4f32-87ea-f7a935ff11b5 · outbound
This paper cites One is to study pre-trained speech models with emotional speech data like Emotion2Vec [21].
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices One is to study pre-trained speech models with emotional speech data like Emotion2Vec [21]
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:20.549635Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 5850a2fc-a867-4c13-a2ac-849fa59219f8 · outbound
This paper cites Experimental results show thatSAILERis highly competitive in the IS25-SER challenge, achieving top-tier per- formance with minimum system complexity.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Experimental results show thatSAILERis highly competitive in the IS25-SER challenge, achieving top-tier per- formance with minimum system complexity
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:20.311721Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 24609332-2203-466b-aec4-a8f6cc3828c1 · outbound
This paper cites an unresolved cited work.
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T13:20:20.049664Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation b999aa93-d0d8-41d5-99a8-b334069d3c1f · outbound
This paper cites On the Opportunities and Risks of Foundation Models.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices On the Opportunities and Risks of Foundation Models
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:14.606471Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a983f20-3cfa-44cf-b17a-b349f5eaa0ba · outbound
This paper cites Dawn of the trans- former era in speech emotion recognition: closing the valence gap,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Dawn of the trans- former era in speech emotion recognition: closing the valence gap,
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:14.730248Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98a68e9d-5fac-437e-8708-35c6241ce53a · outbound
This paper cites An engineering view on emotions and speech: From analysis and pre- dictive models to responsible human-centered applications,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices An engineering view on emotions and speech: From analysis and pre- dictive models to responsible human-centered applications,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:19.790261Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 715acede-1980-437b-b98c-cd09cdf09630 · outbound
This paper cites Interpreting ambiguous emotional expressions,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Interpreting ambiguous emotional expressions,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:19.494734Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation d51fb5bc-dc84-48ef-9ef3-946477cddefb · outbound
This paper cites Balancing speaker- rater fairness for gender-neutral speech emotion recognition,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Balancing speaker- rater fairness for gender-neutral speech emotion recognition,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:19.241102Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 533722fa-54ca-4013-871a-eb8d44ea8fe0 · outbound
This paper cites People make mistakes: Ob- taining accurate ground truth from continuous annotations of sub- jective constructs,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices People make mistakes: Ob- taining accurate ground truth from continuous annotations of sub- jective constructs,
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:15.203661Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c07a0d6f-130d-49a1-a6bc-e59b74cad23f · outbound
This paper cites Odyssey 2024-speech emotion recognition challenge: Dataset, baseline framework, and results,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Odyssey 2024-speech emotion recognition challenge: Dataset, baseline framework, and results,
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:15.334003Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4e5307dc-67e1-405d-875f-0af38ddecf57 · outbound
This paper cites 1st place solution to odyssey emotion recognition chal- lenge task1: Tackling class imbalance problem,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices 1st place solution to odyssey emotion recognition chal- lenge task1: Tackling class imbalance problem,
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:15.410676Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 73a3bd0e-3184-431f-bdd6-ebb40b2cb721 · outbound
This paper cites Double multi-head attention multimodal system for odyssey 2024 speech emotion recognition challenge,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Double multi-head attention multimodal system for odyssey 2024 speech emotion recognition challenge,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:18.865330Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 27b1eeb3-d73b-41ff-ac1c-aa17089dfdc2 · outbound
This paper cites The interspeech 2025 challenge on speech emotion recognition in naturalistic conditions,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices The interspeech 2025 challenge on speech emotion recognition in naturalistic conditions,
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:15.646962Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 01690554-87d8-49f9-9cdc-5c80f8aef3a0 · outbound
This paper cites Robust speech recognition via large-scale weak supervision,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Robust speech recognition via large-scale weak supervision,
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:15.716790Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f41a785-443d-4419-a516-9c880e44361a · outbound
This paper cites Wavlm: Large-scale self- supervised pre-training for full stack speech processing,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Wavlm: Large-scale self- supervised pre-training for full stack speech processing,
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:15.844727Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5449bd4b-aa66-4d6b-a2b4-d510d35ddced · outbound
This paper cites Emotion recognition from speech using wav2vec 2.0 embeddings,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Emotion recognition from speech using wav2vec 2.0 embeddings,
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:15.969237Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 27d37471-8689-431b-a948-7b76055da34c · outbound
This paper cites Foundation model assisted automatic speech emotion recognition: Transcribing, annotating, and aug- menting,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Foundation model assisted automatic speech emotion recognition: Transcribing, annotating, and aug- menting,
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:16.008100Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a39b1281-fd32-475d-b9f1-c93101cc2229 · outbound
This paper cites Peft-ser: On the use of parameter efficient trans- fer learning approaches for speech emotion recognition using pre- trained speech models,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Peft-ser: On the use of parameter efficient trans- fer learning approaches for speech emotion recognition using pre- trained speech models,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:18.518306Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 1e447c6d-9871-4ffd-822f-fd793a4bfe87 · outbound
This paper cites Fusing asr outputs in joint training for speech emotion recognition,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Fusing asr outputs in joint training for speech emotion recognition,
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:16.226675Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e90317c9-057f-4da2-99cf-ef40ba83dc70 · outbound
This paper cites A framework for automatic human emotion classification using emotional profiles,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices A framework for automatic human emotion classification using emotional profiles,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:18.175314Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation e6852bb9-3dae-44ef-bd11-8b908f5c1189 · outbound
This paper cites Minority views matter: Evaluating speech emotion classifiers with human subjective annotations by an all- inclusive aggregation rule,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Minority views matter: Evaluating speech emotion classifiers with human subjective annotations by an all- inclusive aggregation rule,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:17.971781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation bbba9536-d8ce-4f84-8b8b-23c486d8643c · outbound
This paper cites Building naturalistic emotionally bal- anced speech corpus by retrieving emotional speech from existing podcast recordings,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Building naturalistic emotionally bal- anced speech corpus by retrieving emotional speech from existing podcast recordings,
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:16.676817Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60952ef5-2b2c-4f50-8c1b-b7c39107f6f6 · outbound
This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices RoBERTa: A Robustly Optimized BERT Pretraining Approach
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:20:16.762497Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7644212-881b-4548-9ef5-db830f81ec56 · outbound
This paper cites emotion2vec: Self-supervised pre-training for speech emotion representation,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices emotion2vec: Self-supervised pre-training for speech emotion representation,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:17.704764Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 2e2fc5a2-c5cf-4ce2-bcbd-aeb984166f1b · outbound
This paper cites Emix: a data augmentation method for speech emotion recognition,.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Emix: a data augmentation method for speech emotion recognition,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T13:20:17.475229Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Pith citing papers
Observation b16ed2a7-d8e8-4560-942a-bddb759040c3 · inbound
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices cites this paper.
Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices Developing a Top-tier Framework in Naturalistic Conditions Challenge for Categorized Emotion Prediction: From Speech Foundation Models and Learning Objective to Data Augmentation and Engineering Choices
Reference 2
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-07T13:20:17.324877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.