Post-Processing Automatic Transcriptions with Machine Learning for Verbal Fluency Scoring.

ABSTRACT:

Objective

To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers.

Methods

Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain "gold standard" transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions.

Results

For both animal and letter fluency tasks, we achieved good separation of valid ve

SUBMITTER: Bushnell J 

PROVIDER: S-EPMC11171467 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature