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.

翻译：自动言语识别(ASR)是任何完全自动语音致痴呆症检测模型的一个关键组成部分。然而,尽管经过多年的语音识别研究,对于ASR精确度对痴呆症检测的影响却知之甚少。在本文中,我们实验了人为生成的ASR误差的受控量,并调查其对痴呆症检测的影响。我们发现,删除误差对检测性能的影响最大,因为这些误差对语言综合复杂性和话语描述特征的影响。我们显示了两种不同的认知障碍检测数据集之间的普遍趋势。作为结论,我们提议优化ASR,以反映对删除误差的更严厉处罚,以提高痴呆症检测性能。