Spoken Question Answering (SQA) is essential for machines to reply to user's
question by finding the answer span within a given spoken passage. SQA has been
previously achieved without ASR to avoid recognition errors and
Out-of-Vocabulary (OOV) problems. However, the real-world problem of
Open-domain SQA (openSQA), in which the machine needs to first retrieve
passages that possibly contain the answer from a spoken archive in addition,
was never considered. This paper proposes the first known end-to-end framework,
Speech Dense Passage Retriever (SpeechDPR), for the retrieval component of the
openSQA problem. SpeechDPR learns a sentence-level semantic representation by
distilling knowledge from the cascading model of unsupervised ASR (UASR) and
text dense retriever (TDR). No manually transcribed speech data is needed.
Initial experiments showed performance comparable to the cascading model of
UASR and TDR, and significantly better when UASR was poor, verifying this
approach is more robust to speech recognition errors.

提出基于语音密集检索器 (SpeechDPR) 的端到端框架，用于 Open-domain SQA 问题的检索组件，通过从无监督 ASR (UASR) 和文本密集检索器 (TDR) 的级联模型中提取知识来学习句子级语义表示，初步实验表明该方法在语音识别错误较大时表现更为鲁棒。