The contribution of prosody to machine classification of schizophrenia

Tomer Ben Moshe, Ido Ziv*, Nachum Dershowitz, Kfir Bar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We show how acoustic prosodic features, such as pitch and gaps, can be used computationally for detecting symptoms of schizophrenia from a single spoken response. We compare the individual contributions of acoustic and previously-employed text modalities to the algorithmic determination whether the speaker has schizophrenia. Our classification results clearly show that we can extract relevant acoustic features better than those textual ones. We find that, when combined with those acoustic features, textual features improve classification only slightly.

Original languageEnglish
Article number53
JournalSchizophrenia
Volume10
Issue number1
DOIs
StatePublished - Dec 2024

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