Papers by Adrian Szymczak

2 papers
WER we are and WER we think we are (2020.findings-emnlp)

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Challenge: Recent reports of very low word error rates (WERs) achieved by modern automatic speech recognition systems are skepticism towards the accuracy of modern systems.
Approach: They propose to use a dataset to test automatic speech recognition systems . they propose guidelines for creating real-life datasets with high quality annotations .
Outcome: The proposed system achieves 81% of accuracy on human-chatbot interactions compared to the best reported results on human conversations and public benchmarks.
Why Aren’t We NER Yet? Artifacts of ASR Errors in Named Entity Recognition in Spontaneous Speech Transcripts (2023.acl-long)

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Challenge: despite advances in language models, the transcript of spontaneous human-human conversations remains an insurmountable challenge for most models.
Approach: They examine the relationship between ASR and NER errors which limit NER models' ability to recover entity mentions from spontaneous speech transcripts.
Outcome: The proposed model fails even if no word errors are introduced by the ASR . the proposed model's performance deteriorates when applied to the ASL outputs .

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