  Text-based machine comprehension (MC) systems have a wide-range of
applications, and standard corpora exist for developing and evaluating
approaches. There has been far less research on spoken question answering (SQA)
systems. The SQA task considered in this paper is to extract the answer from a
candidate$\text{'}$s spoken response to a question in a prompt-response style
language assessment test. Applying these MC approaches to this SQA task rather
than, for example, off-topic response detection provides far more detailed
information that can be used for further downstream processing. One significant
challenge is the lack of appropriately annotated speech corpora to train
systems for this task. Hence, a transfer-learning style approach is adopted
where a system trained on text-based MC is evaluated on an SQA task with
non-native speakers. Mismatches must be considered between text documents and
spoken responses; non-native spoken grammar and written grammar. In practical
SQA, ASR systems are used, necessitating an investigation of the impact of ASR
errors. We show that a simple text-based ELECTRA MC model trained on SQuAD2.0
transfers well for SQA. It is found that there is an approximately linear
relationship between ASR errors and the SQA assessment scores but grammar
mismatches have minimal impact.
