  End-to-end (E2E) spoken language understanding (SLU) systems avoid an
intermediate textual representation by mapping speech directly into intents
with slot values. This approach requires considerable domain-specific training
data. In low-resource scenarios this is a major concern, e.g., in the present
study dealing with SLU for dysarthric speech. Pretraining part of the SLU model
for automatic speech recognition targets helps but no research has shown to
which extent SLU on dysarthric speech benefits from knowledge transferred from
other dysarthric speech tasks. This paper investigates the efficiency of
pre-training strategies for SLU tasks on dysarthric speech. The designed SLU
system consists of a TDNN acoustic model for feature encoding and a capsule
network for intent and slot decoding. The acoustic model is pre-trained in two
stages: initialization with a corpus of normal speech and finetuning on a
mixture of dysarthric and normal speech. By introducing the intelligibility
score as a metric of the impairment severity, this paper quantitatively
analyzes the relation between generalization and pathology severity for
dysarthric speech.

Audio and Speech Processing