This paper proposes an efficient attempt to noisy speech emotion recognition
(NSER). Conventional NSER approaches have proven effective in mitigating the
impact of artificial noise sources, such as white Gaussian noise, but are
limited to non-stationary noises in real-world environments due to their
complexity and uncertainty. To overcome this limitation, we introduce a new
method for NSER by adopting the automatic speech recognition (ASR) model as a
noise-robust feature extractor to eliminate non-vocal information in noisy
speech. We first obtain intermediate layer information from the ASR model as a
feature representation for emotional speech and then apply this representation
for the downstream NSER task. Our experimental results show that 1) the
proposed method achieves better NSER performance compared with the conventional
noise reduction method, 2) outperforms self-supervised learning approaches, and
3) even outperforms text-based approaches using ASR transcription or the ground
truth transcription of noisy speech.

本文提出了一种高效的噪声语音情感识别（NSER）方法，通过采用自动语音识别（ASR）模型作为噪声鲁棒特征提取器来消除噪声语音中的非语音信息，以解决传统 NSER 方法在真实环境中对非平稳噪声的复杂性和不确定性的限制。实验证明，该方法在 NSER 性能上优于传统的噪声减少方法，胜过自监督学习方法，并且即使在使用 ASR 转录或噪声语音的基准转录的文本方法中也表现优异。