Speech emotion recognition(SER) systems often struggle in real-world environments, where ambient noise severely degrades their performance. This paper explores a novel approach that exploits prior knowledge of testing environments to maximize SER performance under noisy conditions. To address this task, we propose a text-guided, environment-aware training where an SER model is trained with contaminated speech samples and their paired noise description. We use a pre-trained text encoder to extract the text-based environment embedding and then fuse it to a transformer-based SER model during training and inference. We demonstrate the effectiveness of our approach through our experiment with the MSP-Podcast corpus and real-world additive noise samples collected from the Freesound and DEMAND repositories. Our experiment indicates that the text-based environment descriptions processed by alarge language model(LLM) produce representations that improve the noise-robustness of the SER system. With acontrastive learning(CL)-based representation, our proposed method can be improved by jointly fine-tuning the text encoder with the emotion recognition model. Under the -5dBsignal-to-noise ratio(SNR) level, fine-tuning the text encoder improves our CL-based representation method by 76.4% (arousal), 100.0% (dominance), and 27.7% (valence).

Aspeech emotion recognition(SER) system deployed on a real-world application is highly likely to encounter speech contaminated with unconstrained background noise. To deal with this issue, aspeech enhancement(SE) module can be attached to the SER system to compensate for the environmental difference of an input. Although the SE module can improve the quality and intelligibility of a given speech, there is a risk of affecting discriminative acoustic features for SER that are resilient to environmental differences. Exploring this idea, we propose to enhance only weak features that degrade the emotion recognition performance, while keeping strong features that are resilient to environmental differences. Our model first identifies weak feature sets by using multiple models trained with one acoustic feature at a time using clean speech. After training the single-feature models, we rank each speech feature by measuring three criteria: performance, robustness, and a joint rank ranking that combines performance and robustness. We group the weak features by cumulatively incrementing the features from the bottom to the top of each rank. Once the weak feature set is defined, we only enhance those weak features, keeping the resilient features unchanged. We implement these ideas with thelow-level descriptors(LLDs). We show that extracting LLDs from an enhanced speech signal does not improve the performance of weak features. Instead, directly enhancing the LLDs lead to better performance. Our experiment with clean and noisy versions of the MSP-Podcast corpus shows that the selective feature enhancement approach proposed in this study yields a 17.7% (arousal), 21.2% (dominance), and 3.3% (valence) performance gains over a system that enhances all the LLDs for the 10dBsignal-to-noise ratio(SNR) condition.

Studies have shown high performance in the speech emotion recognition (SER) task by fine-tuning a self-supervised speech representation model. Although this model can provide emotionally discriminative embedding in clean conditions, adapting it to a noisy target environment is still required when deployed on real-world applications. For adaptation, it is essential to balance between acquiring new knowledge from noisy speech and keeping the previous knowledge acquired during the pre-training and fine-tuning of the model. Therefore, we propose a contrastive teacher-student learning framework to retrain a self-supervised speech representation model for noisy SER. To keep the knowledge of the original model, we minimize the root mean square error between the clean embeddings from the original SER model and the noisy embeddings from the retrained model. To acquire the discriminative knowledge in the target noisy condition, we also minimize the InfoNCE loss by selecting the corresponding clean embedding as a positive sample and other noisy embeddings with different emotional labels as negative samples. Our experiment with the clean and noisy version of the MSP-Podcast corpus demonstrates that the contrastive teacher-student learning framework can significantly improve the performance of the model only trained with the clean speech in the target noisy condition for all the emotional attributes.

Speech emotion recognition (SER) system deployed in real-world applications often encounters noisy speech. While most noise compensation techniques consider all acoustic features to have equal impact on the SER model, some acoustic features may be more sensitive to noisy conditions. This paper investigates the noise robustness of each feature in the acoustic feature set. We focus on low-level descriptors (LLDs) commonly used in SER systems. We firstly train SER models with clean speech by only using a single LLD. Then, we rank each LLD with respect to the absolute performance on a development set contaminated with noise, and the relative performance decrease from the results from the models trained with the clean set. Our experiment shows that using all the LLDs leads to worse performance than training the system with a single robust LLD. We propose to select a group of robust features according to their performance and robustness in noisy condition. Without using any compensation method, our feature selection methods improve the performance by 24.4% (arousal), 23.9% (dominance), and 43.2% (valence) in the 10dB noisy condition. Moreover, even though the selection is conducted with the 10dB condition, our selection methods also yield performance improvements in unseen noisy recording conditions.