Charting 15 years of progress in deep learning for speech emotion recognition: A replication study | Paper Citation Record

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This paper cites no agreement.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study no agreement

Reference 1

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This paper cites In many cases, OOD UAR is, surprisingly, higher than IID.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study In many cases, OOD UAR is, surprisingly, higher than IID

Reference 2

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This paper cites We computed the centred kernel alignment (CKA) [53], a measure of similarity for hidden representations using EmoDB as a probing dataset due to its smaller size.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study We computed the centred kernel alignment (CKA) [53], a measure of similarity for hidden representations using EmoDB as a probing dataset due to its smaller size

Reference 3

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This paper cites an unresolved cited work.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Unresolved cited work

Reference 4

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This paper cites However, as before, the Spearman’s � between noisy UAR and year of publication (��), MACs ( ��), and � of parameters ( ��) was extremely low.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study However, as before, the Spearman’s � between noisy UAR and year of publication (��), MACs ( ��), and � of parameters ( ��) was extremely low

Reference 5

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This paper cites To do so, we computed the speaker-level performance for each task and used that as the utility to compute the Gini index.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study To do so, we computed the speaker-level performance for each task and used that as the utility to compute the Gini index

Reference 6

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This paper cites neural scaling laws.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study neural scaling laws

Reference 7

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This paper cites Speech emotion recognition: Two decades in a nutshell, benchmarks, and ongoing trends,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Speech emotion recognition: Two decades in a nutshell, benchmarks, and ongoing trends,

Reference 8

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This paper cites Speech emotion recognition using deep learning techniques: A review,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Speech emotion recognition using deep learning techniques: A review,

Reference 9

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This paper cites Odyssey 2024 – speech emotion recognition challenge: Dataset, baseline framework, and results,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Odyssey 2024 – speech emotion recognition challenge: Dataset, baseline framework, and results,

Reference 10

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This paper cites You BEEP Machine – Emotion in Automatic Speech Understanding Systems,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study You BEEP Machine – Emotion in Automatic Speech Understanding Systems,

Reference 11

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This paper cites Emotion recognition in speech using neural networks,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Emotion recognition in speech using neural networks,

Reference 12

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This paper cites Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network,

Reference 13

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This paper cites Dawn of the transformer era in speech emotion recognition: Closing the valence gap,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Dawn of the transformer era in speech emotion recognition: Closing the valence gap,

Reference 14

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This paper cites Hear: Holistic evaluation of audio representations,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Hear: Holistic evaluation of audio representations,

Reference 15

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This paper cites SUPERB: Speech Processing Universal PERformance Benchmark,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study SUPERB: Speech Processing Universal PERformance Benchmark,

Reference 16

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This paper cites Crema-d: Crowd-sourced emotional multimodal actors dataset,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Crema-d: Crowd-sourced emotional multimodal actors dataset,

Reference 17

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This paper cites Iemocap: Interactive emotional dyadic motion capture database,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Iemocap: Interactive emotional dyadic motion capture database,

Reference 18

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This paper cites Probing speech emotion recognition transformers for linguistic knowledge,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Probing speech emotion recognition transformers for linguistic knowledge,

Reference 19

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This paper cites Interspeech 2009 emotion challenge revisited: Benchmarking 15 years of progress in speech emotion recognition,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Interspeech 2009 emotion challenge revisited: Benchmarking 15 years of progress in speech emotion recognition,

Reference 20

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This paper cites Building naturalistic emotionally balanced speech corpus by retrieving emotional speech from existing podcast recordings,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Building naturalistic emotionally balanced speech corpus by retrieving emotional speech from existing podcast recordings,

Reference 21

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This paper cites A database of german emotional speech,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study A database of german emotional speech,

Reference 22

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This paper cites Releasing a thoroughly annotated and processed spontaneous emotional database: The fau aibo emotion corpus,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Releasing a thoroughly annotated and processed spontaneous emotional database: The fau aibo emotion corpus,

Reference 23

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This paper cites The Interspeech 2009 Emotion Challenge,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study The Interspeech 2009 Emotion Challenge,

Reference 24

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This paper cites Sewa db: A rich database for audio-visual emotion and sentiment research in the wild,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Sewa db: A rich database for audio-visual emotion and sentiment research in the wild,

Reference 25

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This paper cites The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,

Reference 26

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This paper cites Emotion recognition using a hierarchical binary decision tree approach,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Emotion recognition using a hierarchical binary decision tree approach,

Reference 27

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This paper cites The bitter lesson,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study The bitter lesson,

Reference 28

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

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study The Geneva minimalistic acoustic parameter set (GeMAPS) for voice research and affective computing,

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This paper cites An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study An Image-based Deep Spectrum Feature Representation for the Recognition of Emotional Speech,

Reference 30

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This paper cites Exploring deep spectrum representations via attention- based recurrent and convolutional neural networks for speech emotion recognition,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Exploring deep spectrum representations via attention- based recurrent and convolutional neural networks for speech emotion recognition,

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This paper cites AST: Audio Spectrogram Transformer,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study AST: Audio Spectrogram Transformer,

Reference 32

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This paper cites Audio set: An ontology and human-labeled dataset for audio events,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Audio set: An ontology and human-labeled dataset for audio events,

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This paper cites ECAPA-TDNN: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study ECAPA-TDNN: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,

Reference 34

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This paper cites Panns: Large-scale pretrained audio neural networks for audio pattern recognition,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Panns: Large-scale pretrained audio neural networks for audio pattern recognition,

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This paper cites The role of task and acoustic similarity in audio transfer learning: Insights from the speech emotion recognition case,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study The role of task and acoustic similarity in audio transfer learning: Insights from the speech emotion recognition case,

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This paper cites Robust speech recognition via large-scale weak supervision,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Robust speech recognition via large-scale weak supervision,

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This paper cites Wav2vec 2.0: A framework for self-supervised learning of speech representations,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Wav2vec 2.0: A framework for self-supervised learning of speech representations,

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This paper cites Hubert: Self-supervised speech representation learning by masked prediction of hidden units,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

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This paper cites “You stupid tin box.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study “You stupid tin box

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This paper cites Steidl, Automatic Classification of Emotion-Related User States in Spontaneous Children’s Speech.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Steidl, Automatic Classification of Emotion-Related User States in Spontaneous Children’s Speech

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This paper cites autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study autrainer: A Modular and Extensible Deep Learning Toolkit for Computer Audition Tasks

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This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Bert: Pre-training of deep bidirectional transformers for language understanding,

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

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study RoBERTa: A Robustly Optimized BERT Pretraining Approach

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This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:30.311960Z

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This paper cites Electra: Pre- training text encoders as discriminators rather than generators,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Electra: Pre- training text encoders as discriminators rather than generators,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:30.063045Z

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This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Llama 2: Open Foundation and Fine-Tuned Chat Models

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This paper cites The Llama 3 Herd of Models.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study The Llama 3 Herd of Models

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This paper cites Mistral 7B.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Mistral 7B

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This paper cites LoRA: Low-rank adaptation of large language models,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study LoRA: Low-rank adaptation of large language models,

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:29.820184Z

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Observation e52ef06c-fed7-4052-8a3f-e26ddb41bfd6 · outbound

This paper cites A curated dataset of urban scenes for audio-visual scene analysis,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study A curated dataset of urban scenes for audio-visual scene analysis,

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This paper cites Enrolment-based person- alisation for improving individual-level fairness in speech emotion recognition,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Enrolment-based person- alisation for improving individual-level fairness in speech emotion recognition,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:29.303436Z

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This paper cites What size test set gives good error rate estimates?.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study What size test set gives good error rate estimates?

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:29.115752Z

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This paper cites A formula for the gini coefficient,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study A formula for the gini coefficient,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:28.911427Z

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This paper cites Shalev-Shwartz and S.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Shalev-Shwartz and S

Reference 55

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This paper cites Underspecification presents challenges for credibility in modern machine learning,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Underspecification presents challenges for credibility in modern machine learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:28.515732Z

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This paper cites On the power of curriculum learning in training deep networks,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study On the power of curriculum learning in training deep networks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:28.335926Z

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This paper cites Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Does the Definition of Difficulty Matter? Scoring Functions and their Role for Curriculum Learning

Reference 58

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This paper cites Scalable hyperparameter transfer learning,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Scalable hyperparameter transfer learning,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:28.084329Z

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This paper cites Similarity of neural network representations revisited,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Similarity of neural network representations revisited,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:27.823419Z

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This paper cites Deep learning of representations for unsupervised and transfer learning,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Deep learning of representations for unsupervised and transfer learning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:27.614547Z

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Observation b90c8b98-a3a0-43cf-86ed-8e479b2a4bb1 · outbound

This paper cites Rethinking CNN Models for Audio Classification.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Rethinking CNN Models for Audio Classification

Reference 62

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Charting 15 years of progress in deep learning for speech emotion recognition: A replication study What is being transferred in transfer learning?

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:27.463993Z

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Observation ee1d35ff-0990-47e3-9d03-5597d71d88a2 · outbound

This paper cites Acoustic profiles in vocal emotion expression.,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Acoustic profiles in vocal emotion expression.,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:27.242890Z

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This paper cites Scaling Laws for Neural Language Models.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Scaling Laws for Neural Language Models

Reference 65

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This paper cites Computer Audition: From Task-Specific Machine Learning to Foundation Models.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Computer Audition: From Task-Specific Machine Learning to Foundation Models

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-06T05:03:22.962325Z

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Observation 9a5d045f-0711-43bc-9225-a0d43aaf3647 · outbound

This paper cites Can large language models aid in annotating speech emotional data? uncovering new frontiers [research frontier],.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Can large language models aid in annotating speech emotional data? uncovering new frontiers [research frontier],

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:27.041394Z

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This paper cites Winner’s curse? on pace, progress, and empirical rigor,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Winner’s curse? on pace, progress, and empirical rigor,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:26.809672Z

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Observation a7902f98-eb6d-4281-8907-7a4ee874fd61 · outbound

This paper cites Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research.,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Troubling trends in machine learning scholarship: Some ml papers suffer from flaws that could mislead the public and stymie future research.,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:26.651612Z

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This paper cites On Empirical Comparisons of Optimizers for Deep Learning.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study On Empirical Comparisons of Optimizers for Deep Learning

Reference 70

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This paper cites Unreproducible research is reproducible,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Unreproducible research is reproducible,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:26.481829Z

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Observation 44ba4015-41eb-4ba4-b92c-df3c81ed36a0 · outbound

This paper cites Beyond deep learning: Charting the next frontiers of affective computing,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Beyond deep learning: Charting the next frontiers of affective computing,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:26.272093Z

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Observation 8c2fd22b-2e59-4996-9d1e-0d968eab38fb · outbound

This paper cites Basic emotions,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Basic emotions,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:26.072177Z

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Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-06T05:03:25.885779Z

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This paper cites End-to-end speech emotion recognition using deep neural networks,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study End-to-end speech emotion recognition using deep neural networks,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:25.724323Z

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Observation 2ca17b07-c16b-4d8a-a2b9-d70213058828 · outbound

This paper cites Speech emotion recognition using deep 1d & 2d cnn lstm networks,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Speech emotion recognition using deep 1d & 2d cnn lstm networks,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:25.558121Z

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Observation 75a9af9f-8537-410b-809c-63da0040fbc1 · outbound

This paper cites V oxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study V oxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:25.367123Z

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Observation 09d67d0f-c6d4-4047-8aa7-96cef23409fd · outbound

This paper cites Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:25.127563Z

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This paper cites Mp3 and aac explained,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Mp3 and aac explained,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:24.894328Z

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Observation 5111cf98-f387-40f5-8be5-29d5bd67e7d4 · outbound

This paper cites High fidelity neural audio compression,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study High fidelity neural audio compression,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:24.511956Z

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Observation f9dce89f-c40a-4e17-bb14-135636930f05 · outbound

This paper cites Semanticodec: An ultra low bitrate semantic audio codec for general sound,.

Charting 15 years of progress in deep learning for speech emotion recognition: A replication study Semanticodec: An ultra low bitrate semantic audio codec for general sound,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:03:24.280485Z

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