Sampling of natural speech for the assessment of psychopathology: data collection procedure and inter-rater reliability

We present a speech collection and processing method that elicits speech samples from participants with a range of psychopathology, suitable for manual and computerized analysis with applications to clinical settings.

Authors

Institutions

Publication Details

Journal
Psychiatry Research
Published
2025-12-11
DOI
https://doi.org/10.1016/j.psychres.2025.116895
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Sampling of natural speech for the assessment of psychopathology: data collection procedure and inter-rater reliability

Michael Kiefte, Martin Alda, D. Yeatman Jason, Rudolf Uher et al.
Psychiatry Research
Emotion and Mood Recognition
article

Sampling of natural speech for the assessment of psychopathology: data collection procedure and inter-rater reliability

Michael Kiefte, Martin Alda, D. Yeatman Jason, Rudolf Uher, Stan Matwin, Sheri Rempel, Katerina Dikaios, Sri Harsha Dumpala, Sageev Oore
article en

Abstract

We present a speech collection and processing method that elicits speech samples from participants with a range of psychopathology, suitable for manual and computerized analysis with applications to clinical settings.

Psychiatry ResearchVol. 356
Dalhousie University (CA), Nova Scotia Health Authority (CA), Vector Institute (CA), McMaster University (CA)
Openalex Percentile: Top 58%
Emotion and Mood Recognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.