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Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others

机译:ASR对阿尔茨海默病检测的影响:所有错误都是平等的,但删除比其他错误更平等

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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.
机译:自动语音识别(ASR)是任何完全自动化的基于语音的痴呆症检测模型的关键组成部分。然而,尽管有多年的语音识别研究,但对ASR准确性对痴呆检测的影响很少。在本文中,我们试验受控量的人工产生的ASR误差并调查它们对痴呆症检测的影响。我们发现删除错误会影响最多的检测性能,因为它们对语言中的句法复杂性和话语代表的特征的影响。我们展示了跨越认知障碍检测的两个不同数据集的趋势。作为结论,我们提出优化ASR以反映缺失误差的更高罚款,以提高痴呆症检测性能。

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