  In a spoken multiple-choice question answering (SMCQA) task, given a passage,
a question, and multiple choices all in the form of speech, the machine needs
to pick the correct choice to answer the question. While the audio could
contain useful cues for SMCQA, usually only the auto-transcribed text is
utilized in system development. Thanks to the large-scaled pre-trained language
representation models, such as the bidirectional encoder representations from
transformers (BERT), systems with only auto-transcribed text can still achieve
a certain level of performance. However, previous studies have evidenced that
acoustic-level statistics can offset text inaccuracies caused by the automatic
speech recognition systems or representation inadequacy lurking in word
embedding generators, thereby making the SMCQA system robust. Along the line of
research, this study concentrates on designing a BERT-based SMCQA framework,
which not only inherits the advantages of contextualized language
representations learned by BERT, but integrates the complementary
acoustic-level information distilled from audio with the text-level
information. Consequently, an audio-enriched BERT-based SMCQA framework is
proposed. A series of experiments demonstrates remarkable improvements in
accuracy over selected baselines and SOTA systems on a published Chinese SMCQA
dataset.
