  Speech contains information that is clinically relevant to some diseases,
which has the potential to be used for health assessment. Recent work shows an
interest in applying deep learning algorithms, especially pretrained large
speech models to the applications of Automatic Speech Assessment. One question
that has not been explored is how these models output the results based on
their inputs. In this work, we train and compare two configurations of Audio
Spectrogram Transformer in the context of Voice Disorder Detection and apply
the attention rollout method to produce model relevance maps, the computed
relevance of the spectrogram regions when the model makes predictions. We use
these maps to analyse how models make predictions in different conditions and
to show that the spread of attention is reduced as a model is finetuned, and
the model attention is concentrated on specific phoneme regions.
