mlsanityData to 13 Oct 2026

Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP

arXiv 2304.00860 ↗First posted 3 Apr 2023cs.SD cs.LG eess.AScited by 6

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Abstract (arXiv)

There is an imminent need for guidelines and standard test sets to allow direct and fair comparisons of speech emotion recognition (SER). While resources, such as the Interactive Emotional Dyadic Motion Capture (IEMOCAP) database, have emerged as widely-adopted reference corpora for researchers to develop and test models for SER, published work reveals a wide range of assumptions and variety in its use that challenge reproducibility and generalization. Based on a critical review of the latest advances in SER using IEMOCAP as the use case, our work aims at two contributions: First, using an analysis of the recent literature, including assumptions made and metrics used therein, we provide a set of SER evaluation guidelines. Second, using recent publications with open-sourced implementations, we focus on reproducibility assessment in SER.

Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). Builds on 19 of its 32 references that are in the map. First posted on arXiv 3 Apr 2023.

Builds on

19 of 32 references.

References · 19

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,870
  2. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,313
  3. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,844
  4. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 657
  5. 2020A Metric Learning Reality Checkcited by 320
  6. 2021Emotion Recognition from Speech Using Wav2vec 2.0 Embeddingscited by 159
  7. 2020Towards Learning a Universal Non-Semantic Representation of Speechcited by 84
  8. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  9. 2021Universal Paralinguistic Speech Representations Using Self-Supervised Conformerscited by 32
  10. 2022Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Informationcited by 31
  11. 2022TRILLsson: Distilled Universal Paralinguistic Speech Representationscited by 16
  12. 2021Light-SERNet: A lightweight fully convolutional neural network for speech emotion recognitioncited by 14
7 more
  1. 2022Speaker Normalization for Self-supervised Speech Emotion Recognitioncited by 14
  2. 2021Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attentioncited by 13
  3. 2021Speech Emotion Recognition with Multiscale Area Attention and Data Augmentationcited by 12
  4. 2020CopyPaste: An Augmentation Method for Speech Emotion Recognitioncited by 10
  5. 2021Speaker Attentive Speech Emotion Recognitioncited by 9
  6. 2017Emotion Recognition From Speech With Recurrent Neural Networkscited by 8
  7. 2022Speech Emotion Recognition with Global-Aware Fusion on Multi-scale Feature Representationcited by 4

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 2.

6 papers.

  1. 2024EmoBox: Multilingual Multi-corpus Speech Emotion Recognition Toolkit and Benchmarkcited by 39
  2. 2024Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learningcited by 2
  3. 2023A Multi-Task, Multi-Modal Approach for Predicting Categorical and Dimensional Emotionscited by 1
  4. 2026Variational decomposition autoencoding improves disentanglement of latent representationscited by 1
  5. 2024Beyond the Labels: Unveiling Text-Dependency in Paralinguistic Speech Recognition Datasetscited by 0
  6. 2026Acoustic Cue Alignment in Audio Language Models for Speech Emotion Recognitioncited by 0