Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 | Paper Citation Record

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This paper cites Early SER relied on hand-crafted features but struggled with real- world generalization [2].

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Early SER relied on hand-crafted features but struggled with real- world generalization [2]

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This paper cites Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025

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This paper cites The hidden states of the last layerLof the text encoder are denoted byZ L T (j)for positions j= 1,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 The hidden states of the last layerLof the text encoder are denoted byZ L T (j)for positions j= 1,

Reference 3

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This paper cites an unresolved cited work.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Unresolved cited work

Reference 4

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This paper cites We report results for unimodal speech models, bi- modal fusion with text, prosodic and spectral feature integra- tion.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 We report results for unimodal speech models, bi- modal fusion with text, prosodic and spectral feature integra- tion

Reference 5

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This paper cites Our evaluation of unimodal models demonstrated the strong performance of Whisper and XEUS, highlighting their robustness for SER in spontaneous speech.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Our evaluation of unimodal models demonstrated the strong performance of Whisper and XEUS, highlighting their robustness for SER in spontaneous speech

Reference 6

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This paper cites We also thank the Artificial Intelligence Lab at Re- cod.ai, the Institute of Computing, University of Campinas.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 We also thank the Artificial Intelligence Lab at Re- cod.ai, the Institute of Computing, University of Campinas

Reference 7

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This paper cites Affective computing mit press,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Affective computing mit press,

Reference 8

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

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Iemocap: Interactive emotional dyadic motion capture database,

Reference 9

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This paper cites Every rating matters: Joint learning of subjective labels and individual annotators for speech emotion classification,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Every rating matters: Joint learning of subjective labels and individual annotators for speech emotion classification,

Reference 10

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This paper cites Speech emotion recognition using self-supervised features,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Speech emotion recognition using self-supervised features,

Reference 11

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This paper cites Chakraborty, M.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Chakraborty, M

Reference 12

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This paper cites Speech emotion recognition with multi-task learning,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Speech emotion recognition with multi-task learning,

Reference 13

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This paper cites Improving speech emotion recogni- tion using self-supervised learning with domain-specific audiovi- sual tasks,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Improving speech emotion recogni- tion using self-supervised learning with domain-specific audiovi- sual tasks,

Reference 14

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

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Odyssey 2024-speech emotion recognition challenge: Dataset, baseline framework, and results,

Reference 15

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

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 16

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

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,

Reference 17

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

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Wavlm: Large-scale self- supervised pre-training for full stack speech processing,

Reference 18

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

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Robust speech recognition via large-scale weak supervision,

Reference 19

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This paper cites Towards robust speech representation learning for thousands of languages,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Towards robust speech representation learning for thousands of languages,

Reference 20

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Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 A robustly optimized BERT pre-training approach with post-training,

Reference 21

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Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 SeqAug: Sequential Feature Resampling as a modality agnostic augmentation method

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This paper cites Enhancing cross-language multimodal emotion recognition with dual attention transform- ers,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Enhancing cross-language multimodal emotion recognition with dual attention transform- ers,

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This paper cites Ced: Con- sistent ensemble distillation for audio tagging,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Ced: Con- sistent ensemble distillation for audio tagging,

Reference 25

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This paper cites HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition

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Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Graph attention networks,

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Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 GLU Variants Improve Transformer

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This paper cites Espnet: End-to-end speech pro- cessing toolkit,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Espnet: End-to-end speech pro- cessing toolkit,

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This paper cites Less is more: Accu- rate speech recognition & translation without web-scale data,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Less is more: Accu- rate speech recognition & translation without web-scale data,

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This paper cites 1st place solution to odyssey emotion recognition chal- lenge task1: Tackling class imbalance problem,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 1st place solution to odyssey emotion recognition chal- lenge task1: Tackling class imbalance problem,

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Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Fundamental frequency ex- traction in speech emotion recognition,

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Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Autoregressive neural f0 model for statistical parametric speech synthesis,

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This paper cites Rmvpe: A robust model for vocal pitch estimation in polyphonic music,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Rmvpe: A robust model for vocal pitch estimation in polyphonic music,

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This paper cites Enhancing skin can- cer diagnosis using swin transformer with hybrid shifted window- based multi-head self-attention and swiglu-based mlp,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Enhancing skin can- cer diagnosis using swin transformer with hybrid shifted window- based multi-head self-attention and swiglu-based mlp,

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This paper cites Searching for activation functions,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Searching for activation functions,

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This paper cites Decoupled weight de- cay regularization,.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Decoupled weight de- cay regularization,

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

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Panns: Large-scale pretrained audio neural networks for audio pattern recognition,

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Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Focal loss for dense object detection,

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This paper cites Multilingual E5 Text Embeddings: A Technical Report.

Enhancing Speech Emotion Recognition with Graph-Based Multimodal Fusion and Prosodic Features for the Speech Emotion Recognition in Naturalistic Conditions Challenge at Interspeech 2025 Multilingual E5 Text Embeddings: A Technical Report

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