AI Article Synopsis
- Researchers demonstrate that acoustic features, like pitch and pauses, can effectively identify schizophrenia symptoms from a single spoken response.
- They compare the effectiveness of acoustic features to previously used text-based features in their detection algorithms.
- Results indicate that acoustic features are more informative for classification, and while adding text features helps a bit, the improvement is minimal.
Article 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102498 | PMC |
| http://dx.doi.org/10.1038/s41537-024-00463-3 | DOI Listing |
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