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 repository. Our experiment indicates that the text-based environment descriptions processed by a large language model (LLM) produce representations that improve the noise-robustness of the SER system. In addition, our proposed approach with an LLM yields better performance than our environment-agnostic baselines, especially in low signal-to-noise ratio (SNR) conditions. When testing at -5dB SNR level, our proposed method shows better performance than our best baseline model by 31.8 % (arousal), 23.5% (dominance), and 9.5% (valence).

本文针对语音情感识别系统在嘈杂环境中性能不足的问题，提出了一种利用环境文本描述进行训练的新方法。通过将文本描述与带噪语音样本相结合，实验结果表明，该方法在低信噪比条件下显著提高了识别性能，尤其在-5dB信噪比测试中，提升幅度达到31.8 %（唤起）、23.5%（主导感）和9.5%（情感价）。

描述您所在的地方：通过环境文本描述提高语音情感识别的抗噪声能力