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Evaluating Web-Based Automatic Transcription for Alzheimer Speech Data: Transcript Comparison and Machine Learning

Thomas Soroski¹, Thiago da Cunha Vasco², Sally Newton-Mason¹

¹Vancouver Stroke Program and Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.

|September 21, 2022

Summary

This summary is machine-generated.

Automatic speech transcription shows potential for diagnosing neurodegenerative diseases like Alzheimer's disease (AD) and mild cognitive impairment (MCI). Human verification of transcripts significantly improves classification accuracy for spontaneous speech tasks, enhancing diagnostic capabilities.

Keywords:

Alzheimer disease machine learning memory mild cognitive impairment natural language processing neurodegenerative disease speech speech recognition software transcription software

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Area of Science:

Speech analysis
Computational linguistics
Neuroscience

Background:

Speech data offers a non-invasive method for collecting large volumes of information for medical research.
Speech analysis shows promise in diagnosing neurodegenerative diseases.
Accurate transcription of speech is crucial for extracting valuable lexical content.

Purpose of the Study:

To evaluate the utility of automatic speech transcription for classifying neurodegenerative diseases.
To compare the performance of automatically generated transcripts versus manually corrected transcripts for disease classification.
To assess the impact of human verification on machine learning models for neurodegenerative disease detection.

Main Methods:

Recruited participants with Alzheimer disease (AD), mild cognitive impairment (MCI), subjective memory complaints (SMC), and healthy controls.
Collected speech data through picture description, paragraph reading, and life experience recall.
Compared Google speech-to-text software transcripts with manually verified transcripts using error rates and classification accuracy.

Main Results:

Automatic transcription software exhibited higher confidence and lower error rates for healthy controls compared to patients.
Machine learning models using human-verified transcripts significantly outperformed those using automatic transcripts for spontaneous speech.
No significant difference in classification performance was observed for the reading task, and manual pause addition did not impact results.

Conclusions:

Automatically transcribed speech data can differentiate individuals with AD, MCI, or SMC from controls.
Human verification of automatic transcripts is recommended to enhance performance, particularly for spontaneous speech tasks.
Focusing human verification on error correction and punctuation addition, while omitting pauses, can streamline data processing for large-scale analysis.