  We analyze the impact of speaker adaptation in end-to-end automatic speech
recognition models based on transformers and wav2vec 2.0 under different noise
conditions. By including speaker embeddings obtained from x-vector and
ECAPA-TDNN systems, as well as i-vectors, we achieve relative word error rate
improvements of up to 16.3% on LibriSpeech and up to 14.5% on Switchboard. We
show that the proven method of concatenating speaker vectors to the acoustic
features and supplying them as auxiliary model inputs remains a viable option
to increase the robustness of end-to-end architectures. The effect on
transformer models is stronger, when more noise is added to the input speech.
The most substantial benefits for systems based on wav2vec 2.0 are achieved
under moderate or no noise conditions. Both x-vectors and ECAPA-TDNN embeddings
outperform i-vectors as speaker representations. The optimal embedding size
depends on the dataset and also varies with the noise condition.
