On the Impact of Speech Recognition Errors in Passage Retrieval for
Spoken Question Answering
- URL: http://arxiv.org/abs/2209.12944v1
- Date: Mon, 26 Sep 2022 18:29:36 GMT
- Title: On the Impact of Speech Recognition Errors in Passage Retrieval for
Spoken Question Answering
- Authors: Georgios Sidiropoulos, Svitlana Vakulenko, and Evangelos Kanoulas
- Abstract summary: We study the robustness of lexical and dense retrievers against questions with synthetic ASR noise.
We create a new dataset with questions voiced by human users and use their transcriptions to show that the retrieval performance can further degrade when dealing with natural ASR noise instead of synthetic ASR noise.
- Score: 13.013751306590303
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Interacting with a speech interface to query a Question Answering (QA) system
is becoming increasingly popular. Typically, QA systems rely on passage
retrieval to select candidate contexts and reading comprehension to extract the
final answer. While there has been some attention to improving the reading
comprehension part of QA systems against errors that automatic speech
recognition (ASR) models introduce, the passage retrieval part remains
unexplored. However, such errors can affect the performance of passage
retrieval, leading to inferior end-to-end performance. To address this gap, we
augment two existing large-scale passage ranking and open domain QA datasets
with synthetic ASR noise and study the robustness of lexical and dense
retrievers against questions with ASR noise. Furthermore, we study the
generalizability of data augmentation techniques across different domains; with
each domain being a different language dialect or accent. Finally, we create a
new dataset with questions voiced by human users and use their transcriptions
to show that the retrieval performance can further degrade when dealing with
natural ASR noise instead of synthetic ASR noise.
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