1. Speech distortion is extremely detrimental to ASR models while human listeners may not be able to notice. Noise reduction models such as RNNNoise and DeepFilterNet try to reduce "perceptible" noises. And doing that will create imperceptible distortion which ASR models does not like at all.

2. Many noise reduction models apply on raw spectrograms or ERB band such as RNNNoise and DeepFilterNet (equivalent rectangular bands). On the other hands, ASR models mostly run on melspectrograms. This mismatch tends to create problem. I have seen many papers from Google claimed that reducing noise in melspectrogram often helps their keyword spotting.