\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。

有用的失误：自动语音识别错误是否可以提高下游痴呆分类？

Useful Blunders: Can Automated Speech Recognition Errors Improve  Downstream Dementia Classification?

Linguistic anomalies detectable in spontaneous speech have shown promise for
various clinical applications including screening for dementia and other forms
of cognitive impairment. The feasibility of deploying automated tools that can
classify language samples obtained from speech in large-scale clinical settings
depends on the ability to capture and automatically transcribe the speech for
subsequent analysis. However, the impressive performance of self-supervised
learning (SSL) automatic speech recognition (ASR) models with curated speech
data is not apparent with challenging speech samples from clinical settings.
One of the key questions for successfully applying ASR models for clinical
applications is whether imperfect transcripts they generate provide sufficient
information for downstream tasks to operate at an acceptable level of accuracy.
In this study, we examine the relationship between the errors produced by
several deep learning ASR systems and their impact on the downstream task of
dementia classification. One of our key findings is that, paradoxically, ASR
systems with relatively high error rates can produce transcripts that result in
better downstream classification accuracy than classification based on verbatim
transcripts.

研究使用自我监督学习的深度学习自动语音识别模型在临床设置中处理话语自然性上的困难，并探讨产生的错误对痴呆分类下游任务准确性的影响。结果表明，相对高错误率的自动语音识别系统可以产生更好的下游分类精度而非字面上的听写结果。