TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition
<|reference_start|>TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition: One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in deteriorating SER performance in practice. In this paper, we introduce a Two-level Refinement Network, dubbed TRNet, to address this challenge. Specifically, a pre-trained speech enhancement module is employed for front-end noise reduction and noise level estimation. Later, we utilize clean speech spectrograms and their corresponding deep representations as reference signals to refine the spectrogram distortion and representation shift of enhanced speech during model training. Experimental results validate that the proposed TRNet substantially promotes the robustness of the proposed system in both matched and unmatched noisy environments, without compromising its performance in noise-free environments.<|reference_end|>
@article{chen2024trnet:,
    title={TRNet: Two-level Refinement Network leveraging Speech Enhancement for
  Noise Robust Speech Emotion Recognition},
    author={Chengxin Chen, Pengyuan Zhang},
    journal={Applied Acoustics,2024,225:110169},
    year={2024},
    doi={10.1016/j.apacoust.2024.110169},
    archivePrefix={arXiv},
    eprint={2404.12979},
    primaryClass={cs.SD cs.LG eess.AS}
}