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Evaluating Web-Based Automatic Transcription for Alzheimer’s Speech Data: Transcript Comparison and Machine Learning Analysis (Preprint)


 BACKGROUND
 Speech data for medical research can be collected non-invasively and in large volumes. Speech analysis has shown promise in diagnosing neurodegenerative disease. To effectively leverage speech data, transcription is important as there is valuable information contained in lexical content. Manual transcription, while highly accurate, limits potential scalability and cost savings associated with language-based screening.
 
 
 OBJECTIVE
 To better understand the use of automatic transcription for classification of neurodegenerative disease (Alzheimer’s Disease [AD], mild cognitive impairment [MCI] or subjective memory complaints [SMC] versus healthy controls), we compared automatically generated transcripts against transcripts that went through manual correction.
 
 
 METHODS
 We recruited individuals from a memory clinic (“patients”) with a diagnosis of mild-moderate AD, (n=44), MCI (n=20), SMC (n=8) and healthy controls living in the community (n=77). Participants were asked to describe a standardized picture, read a paragraph, and recall a pleasant life experience. We compared transcripts generated using Google speech-to-text software to manually-verified transcripts by examining transcription confidence scores, transcription error rates, and machine learning classification accuracy. For the classification tasks, Logistic Regression, Gaussian Naive Bayes, and Random Forests were used.
 
 
 RESULTS
 The transcription software showed higher confidence scores (P<.001) and lower error rates (P>.05) for speech from healthy controls as compared with patients. Classification models using human-verified transcripts significantly (P<.001) outperformed automatically-generated transcript models for both spontaneous speech tasks. This comparison showed no difference in the reading task. Manually adding pauses to transcripts had no impact on classification performance. Manually correcting both spontaneous speech tasks led to significantly higher performances in the machine learning models.
 
 
 CONCLUSIONS
 We found that automatically-transcribed speech data could be used to distinguish patients with a diagnosis of AD, MCI or SMC from controls. We recommend a human verification step to improve the performance of automatic transcripts, especially for spontaneous tasks. Moreover, human verification can focus on correcting errors and adding punctuation to transcripts. Manual addition of pauses, however, is not needed, which can simplify the human verification step to more efficiently process large volumes of speech data.
