Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research
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 f...
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| Published in: | Frontiers in psychology Vol. 11; p. 1726 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Frontiers Media S.A
28-07-2020
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | 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. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology Edited by: Giuseppe Sartori, University of Padua, Italy Reviewed by: Cristina Mazza, Sapienza University of Rome, Italy; Graziella Orrù, University of Pisa, Italy |
| ISSN: | 1664-1078 1664-1078 |
| DOI: | 10.3389/fpsyg.2020.01726 |