Using feature selection to evaluate pathological speech after training with a serious game
DOI:
https://doi.org/10.36505/ExLing-2021/12/0062/000535Keywords:
Parkinson’s disease, pathological speech, feature selectionAbstract
To evaluate the effectiveness of speech therapy, speech features before and after treatment can be compared, focussing on those features that changed most during treatment. In the current study acoustic features were automatically extracted from speech of patients affected by Parkinson’s Disease who had received speech treatment. Praat and openSMILE were used for feature extraction. Through feature selection, the top ten most characterizing features for pre vs. post-treatment were found. Further analysis of these features confirmed that after treatment the speakers spoke louder with lower pitch, which were the goals of the treatment.
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Articles are published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
