mlsanityData to 14 Oct 2026

Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancement

arXiv 2309.01164 ↗First posted 3 Sep 2023eess.AS cs.LG cs.SDcited by 5

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Abstract (arXiv)

Speech emotion recognition (SER) often experiences reduced performance due to background noise. In addition, making a prediction on signals with only background noise could undermine user trust in the system. In this study, we propose a Noise Robust Speech Emotion Recognition system, NRSER. NRSER employs speech enhancement (SE) to effectively reduce the noise in input signals. Then, the signal-to-noise-ratio (SNR)-level detection structure and waveform reconstitution strategy are introduced to reduce the negative impact of SE on speech signals with no or little background noise. Our experimental results show that NRSER can effectively improve the noise robustness of the SER system, including preventing the system from making emotion recognition on signals consisting solely of background noise. Moreover, the proposed SNR-level detection structure can be used individually for tasks such as data selection.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 3 Sep 2023.

Builds on

9 of 20 references.

References · 9

  1. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  2. 2022Dawn of the transformer era in speech emotion recognition: closing the valence gapcited by 157
  3. 2022CMGAN: Conformer-based Metric GAN for Speech Enhancementcited by 45
  4. 2020Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancementcited by 19
  5. 2021Contrastive Unsupervised Learning for Speech Emotion Recognitioncited by 19
  6. 2021DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancementcited by 14
  7. 2021Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberationcited by 13
  8. 2022Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognitioncited by 9
  9. 2021SNRi Target Training for Joint Speech Enhancement and Recognitioncited by 4

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Cited by year (2026 to 14 Oct)

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

5 papers.

  1. 2026MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signalscited by 1
  2. 2024TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognitioncited by 0
  3. 2025Joint Learning using Mixture-of-Expert-Based Representation for Speech Enhancement and Robust Emotion Recognitioncited by 0
  4. 2025Explainable Transformer-CNN Fusion for Noise-Robust Speech Emotion Recognitioncited by 0
  5. 2026Training-Free Intelligibility-Guided Observation Addition for Noisy ASRcited by 0