# Mitigating the Impact of Speech Recognition Errors on Spoken Question Answering by Adversarial Domain Adaptation

arXiv: https://arxiv.org/abs/1904.07904 · HTML: https://mlsanity.com/paper/1904.07904/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering](https://mlsanity.com/paper/2109.03381/) (2021)
- [End-to-end Spoken Conversational Question Answering: Task, Dataset and Model](https://mlsanity.com/paper/2204.14272/) (2022)
- [HeySQuAD: A Spoken Question Answering Dataset](https://mlsanity.com/paper/2304.13689/) (2023)
- [DUAL: Discrete Spoken Unit Adaptive Learning for Textless Spoken Question Answering](https://mlsanity.com/paper/2203.04911/) (2022)
- [Zero-Shot End-To-End Spoken Question Answering In Medical Domain](https://mlsanity.com/paper/2406.05876/) (2024)
- [End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering](https://mlsanity.com/paper/2511.09282/) (2025)

## Abstract (arXiv)

Spoken question answering (SQA) is challenging due to complex reasoning on top of the spoken documents. The recent studies have also shown the catastrophic impact of automatic speech recognition (ASR) errors on SQA. Therefore, this work proposes to mitigate the ASR errors by aligning the mismatch between ASR hypotheses and their corresponding reference transcriptions. An adversarial model is applied to this domain adaptation task, which forces the model to learn domain-invariant features the QA model can effectively utilize in order to improve the SQA results. The experiments successfully demonstrate the effectiveness of our proposed model, and the results are better than the previous best model by 2% EM score.