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MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition

arXiv 2306.09361 ↗First posted 12 Jun 2023eess.AS cs.CL cs.SDcited by 0

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Abstract (arXiv)

Speech Emotion Recognition (SER) is an important research topic in human-computer interaction. Many recent works focus on directly extracting emotional cues through pre-trained knowledge, frequently overlooking considerations of appropriateness and comprehensiveness. Therefore, we propose a novel framework for pre-training knowledge in SER, called Multi-perspective Fusion Search Network (MFSN). Considering comprehensiveness, we partition speech knowledge into Textual-related Emotional Content (TEC) and Speech-related Emotional Content (SEC), capturing cues from both semantic and acoustic perspectives, and we design a new architecture search space to fully leverage them. Considering appropriateness, we verify the efficacy of different modeling approaches in capturing SEC and fills the gap in current research. Experimental results on multiple datasets demonstrate the superiority of MFSN.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 12 Jun 2023.

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References · 13

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  3. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  4. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
  5. 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 340
  6. 2020TERA: Self-Supervised Learning of Transformer Encoder Representation for Speechcited by 188
  7. 2021Emotion Recognition from Speech Using Wav2vec 2.0 Embeddingscited by 162
  8. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  9. 2022Speech Emotion Recognition with Co-Attention based Multi-level Acoustic Informationcited by 32
  10. 2020Emotion recognition by fusing time synchronous and time asynchronous representationscited by 26
  11. 2020Learning Fine-Grained Cross Modality Excitement for Speech Emotion Recognitioncited by 11
  12. 2022CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion Recognitioncited by 8
1 more
  1. 2022Disentangling Prosody Representations with Unsupervised Speech Reconstructioncited by 8

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