@inproceedings{balagopalan-etal-2020-impact,
    title = "Impact of {ASR} on {A}lzheimer{'}s Disease Detection: All Errors are Equal, but Deletions are More Equal than Others",
    author = "Balagopalan, Aparna  and
      Shkaruta, Ksenia  and
      Novikova, Jekaterina",
    booktitle = "Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wnut-1.21",
    doi = "10.18653/v1/2020.wnut-1.21",
    pages = "159--164",
    abstract = "Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, little is known about the impact of ASR accuracy on dementia detection. In this paper, we experiment with controlled amounts of artificially generated ASR errors and investigate their influence on dementia detection. We find that deletion errors affect detection performance the most, due to their impact on the features of syntactic complexity and discourse representation in speech. We show the trend to be generalisable across two different datasets for cognitive impairment detection. As a conclusion, we propose optimising the ASR to reflect a higher penalty for deletion errors in order to improve dementia detection performance.",
}