  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.
