\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.

不完整的自动语音识别 ASR 转录有效捕捉与痴呆症相关的语言异常，提高分类任务的准确性。ASR 和分类模型之间的协同作用凸显了 ASR 在评估认知损害和相关临床应用中的潜在价值。