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

arXiv: https://arxiv.org/abs/2309.01164 · HTML: https://mlsanity.com/paper/2309.01164/

## 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 (most cited first, 9 of 9)

- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [CMGAN: Conformer-based Metric GAN for Speech Enhancement](https://mlsanity.com/paper/2203.15149/) (2022)
- [Weighted Speech Distortion Losses for Neural-network-based Real-time Speech Enhancement](https://mlsanity.com/paper/2001.10601/) (2020)
- [Contrastive Unsupervised Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2102.06357/) (2021)
- [DPCRN: Dual-Path Convolution Recurrent Network for Single Channel Speech Enhancement](https://mlsanity.com/paper/2107.05429/) (2021)
- [Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation](https://mlsanity.com/paper/2111.06015/) (2021)
- [Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition](https://mlsanity.com/paper/2201.03881/) (2022)
- [SNRi Target Training for Joint Speech Enhancement and Recognition](https://mlsanity.com/paper/2111.00764/) (2021)

## Built on by (most cited first, 5 of 5)

- [MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals](https://mlsanity.com/paper/2603.08174/) (2026)
- [TRNet: Two-level Refinement Network leveraging Speech Enhancement for Noise Robust Speech Emotion Recognition](https://mlsanity.com/paper/2404.12979/) (2024)
- [Joint Learning using Mixture-of-Expert-Based Representation for Speech Enhancement and Robust Emotion Recognition](https://mlsanity.com/paper/2509.08470/) (2025)
- [Explainable Transformer-CNN Fusion for Noise-Robust Speech Emotion Recognition](https://mlsanity.com/paper/2512.18298/) (2025)
- [Training-Free Intelligibility-Guided Observation Addition for Noisy ASR](https://mlsanity.com/paper/2602.20967/) (2026)

## 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.