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Interacting with a speech interface to query a Question Answering (QA) systemis becoming increasingly popular. Typically, QA systems rely on passageretrieval to select candidate contexts and reading comprehension to extract thefinal answer. While there has been some attention to improving the readingcomprehension part of QA systems against errors that automatic speechrecognition (ASR) models introduce, the passage retrieval part remainsunexplored. However, such errors can affect the performance of passageretrieval, leading to inferior end-to-end performance. To address this gap, weaugment two existing large-scale passage ranking and open domain QA datasetswith synthetic ASR noise and study the robustness of lexical and denseretrievers against questions with ASR noise. Furthermore, we study thegeneralizability of data augmentation techniques across different domains; witheach domain being a different language dialect or accent. Finally, we create anew dataset with questions voiced by human users and use their transcriptionsto show that the retrieval performance can further degrade when dealing withnatural ASR noise instead of synthetic ASR noise.

On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question Answering