Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection
This work addresses the problem of error propagation in speech-based Alzheimer's detection for healthcare applications, but it is incremental as it builds on prior findings about non-linear error impacts.
The study investigated how different types of speech recognition errors affect Alzheimer's disease detection using BERT-based systems, finding that errors in keywords are more critical than those in stopwords for detection performance.
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.