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On the Contribution of Lexical Features to Speech Emotion Recognition

arXiv 2509.05634 ↗First posted 6 Sep 2025eess.AS cs.CL cs.SDcited by 0

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Abstract (arXiv)

Although paralinguistic cues are often considered the primary drivers of speech emotion recognition (SER), we investigate the role of lexical content extracted from speech and show that it can achieve competitive and in some cases higher performance compared to acoustic models. On the MELD dataset, our lexical-based approach obtains a weighted F1-score (WF1) of 51.5%, compared to 49.3% for an acoustic-only pipeline with a larger parameter count. Furthermore, we analyze different self-supervised (SSL) speech and text representations, conduct a layer-wise study of transformer-based encoders, and evaluate the effect of audio denoising.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 23 references that are in the map. First posted on arXiv 6 Sep 2025.

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References · 18

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  5. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  6. 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 643
  7. 2023Seamless: Multilingual Expressive and Streaming Speech Translationcited by 192
  8. 2020Real Time Speech Enhancement in the Waveform Domaincited by 186
  9. 2022SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speechcited by 20
  10. 2023SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processingcited by 16
  11. 2023DST: Deformable Speech Transformer for Emotion Recognitioncited by 11
  12. 2024Temporal-Frequency State Space Duality: An Efficient Paradigm for Speech Emotion Recognitioncited by 10
6 more
  1. 2023DWFormer: Dynamic Window transFormer for Speech Emotion Recognitioncited by 7
  2. 2023cross-modal fusion techniques for utterance-level emotion recognition from text and speechcited by 5
  3. 2023A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversioncited by 5
  4. 2025TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakescited by 5
  5. 2025Unmasking real-world audio deepfakes: A data-centric approachcited by 5
  6. 2023An analysis of large speech models-based representations for speech emotion recognitioncited by 1

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