  Text encodings from automatic speech recognition (ASR) transcripts and audio
representations have shown promise in speech emotion recognition (SER) ever
since. Yet, it is challenging to explain the effect of each information stream
on the SER systems. Further, more clarification is required for analysing the
impact of ASR's word error rate (WER) on linguistic emotion recognition per se
and in the context of fusion with acoustic information exploitation in the age
of deep ASR systems. In order to tackle the above issues, we create transcripts
from the original speech by applying three modern ASR systems, including an
end-to-end model trained with recurrent neural network-transducer loss, a model
with connectionist temporal classification loss, and a wav2vec framework for
self-supervised learning. Afterwards, we use pre-trained textual models to
extract text representations from the ASR outputs and the gold standard. For
extraction and learning of acoustic speech features, we utilise openSMILE,
openXBoW, DeepSpectrum, and auDeep. Finally, we conduct decision-level fusion
on both information streams -- acoustics and linguistics. Using the best
development configuration, we achieve state-of-the-art unweighted average
recall values of $73.6\,\%$ and $73.8\,\%$ on the speaker-independent
development and test partitions of IEMOCAP, respectively.
