\textbf{Objectives}: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the ``Cookie Theft'' picture description task. We aimed to assess whether imperfect ASR-generated transcripts could provide valuable information for distinguishing between language samples from cognitively healthy individuals and those with Alzheimer's disease (AD). \textbf{Methods}: We conducted experiments using various ASR models, refining their transcripts with post-editing techniques. Both these imperfect ASR transcripts and manually transcribed ones were used as inputs for the downstream dementia classification. We conducted comprehensive error analysis to compare model performance and assess ASR-generated transcript effectiveness in dementia classification. \textbf{Results}: Imperfect ASR-generated transcripts surprisingly outperformed manual transcription for distinguishing between individuals with AD and those without in the ``Cookie Theft'' task. These ASR-based models surpassed the previous state-of-the-art approach, indicating that ASR errors may contain valuable cues related to dementia. The synergy between ASR and classification models improved overall accuracy in dementia classification. \textbf{Conclusion}: Imperfect ASR transcripts effectively capture linguistic anomalies linked to dementia, improving accuracy in classification tasks. This synergy between ASR and classification models underscores ASR's potential as a valuable tool in assessing cognitive impairment and related clinical applications.

翻译：\textbf{目标}：本研究旨在探究自动语音识别系统产生的错误如何影响痴呆症分类准确性，具体针对“偷饼干”图片描述任务。我们评估了不完美的ASR生成转录在区分认知健康个体与阿尔茨海默病语言样本方面的信息价值。\textbf{方法}：我们使用多种ASR模型进行实验，通过后期编辑技术优化其转录文本。将不完美的ASR转录文本与人工转录文本同时作为下游痴呆症分类的输入，通过全面错误分析比较模型性能，评估ASR生成转录在痴呆症分类中的有效性。\textbf{结果}：在“偷饼干”任务中，不完美的ASR生成转录反而比人工转录更有效地区分AD患者与非AD个体。这些基于ASR的模型超越了先前的最优方法，表明ASR错误可能包含与痴呆症相关的有价值线索。ASR与分类模型的协同作用提升了痴呆症分类的整体准确率。\textbf{结论}：不完美的ASR转录能够有效捕捉与痴呆症相关的语言异常，提高分类任务准确率。ASR与分类模型的这种协同作用凸显了ASR在认知障碍评估及相关临床应用中作为重要工具的潜力。