发现语音识别错误并非都影响阿尔茨海默病检测，关键词错误更致命。
Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection
- 分析不同错误对BERT模型检测的影响，发现错误价值不均等。
- 停用词错误占比高但对诊断帮助小，关键词错误影响显著。
- 适合关注语音识别与疾病检测融合的研究者阅读。
自动语音识别（ASR）在基于语音的阿尔茨海默病（AD）自动检测中起关键作用。然而，识别错误可能在下游任务中传播，影响检测决策。近期研究发现词错误率（WER）与AD检测性能之间存在非线性关系：即使有明显错误的ASR转录文本，其检测准确率仍可媲美人工转录。本文针对基于BERT的AD检测系统，开展一系列分析，探究ASR转录错误的影响。结果表明，并非所有错误对检测性能的影响相同。例如，停用词虽占错误总量较大比例，但在区分AD方面作用有限；而与诊断任务相关的关键词则具有显著更高的重要性。该发现揭示了ASR错误与下游检测模型之间的相互作用机制。
原文摘要 · Abstract (English)
Automatic Speech Recognition (ASR) plays an important role in speech-based automatic detection of Alzheimer's disease (AD). However, recognition errors could propagate downstream, potentially impacting the detection decisions. Recent studies have revealed a non-linear relationship between word error rates (WER) and AD detection performance, where ASR transcriptions with notable errors could still yield AD detection accuracy equivalent to that based on manual transcriptions. This work presents a series of analyses to explore the effect of ASR transcription errors in BERT-based AD detection systems. Our investigation reveals that not all ASR errors contribute equally to detection performance. Certain words, such as stopwords, despite constituting a large proportion of errors, are shown to play a limited role in distinguishing AD. In contrast, the keywords related to diagnosis tasks exhibit significantly greater importance relative to other words. These findings provide insights into the interplay between ASR errors and the downstream detection model.
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