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Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research
Journal
Frontiers in Psychology
Volume
11
Pages / Article-Number
1726
Keywords
Dyadic Speaker Diarization, Psychotherapy Research
Abstract
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.
