Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research.
EMRAI speech corpus
dyadic audio analysis
psychotherapy process measure
random forest
supervised speaker diarization
Journal
Frontiers in psychology
ISSN: 1664-1078
Titre abrégé: Front Psychol
Pays: Switzerland
ID NLM: 101550902
Informations de publication
Date de publication:
2020
2020
Historique:
received:
10
02
2020
accepted:
23
06
2020
entrez:
28
8
2020
pubmed:
28
8
2020
medline:
28
8
2020
Statut:
epublish
Résumé
Speaker diarization is the practice of determining who speaks when in audio recordings. Psychotherapy research often relies on labor intensive manual diarization. Unsupervised methods are available but yield higher error rates. We present a method for supervised speaker diarization based on random forests. It can be considered a compromise between commonly used labor-intensive manual coding and fully automated procedures. The method is validated using the EMRAI synthetic speech corpus and is made publicly available. It yields low diarization error rates (M: 5.61%, STD: 2.19). Supervised speaker diarization is a promising method for psychotherapy research and similar fields.
Identifiants
pubmed: 32849033
doi: 10.3389/fpsyg.2020.01726
pmc: PMC7399377
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1726Informations de copyright
Copyright © 2020 Fürer, Schenk, Roth, Steppan, Schmeck and Zimmermann.
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