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

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