ArticleFrontiers in psychology2020
Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research.
Article in Frontiers in psychology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library.Sensors (Basel, Switzerland) · 2023Article
- Article
- While the Chatbot's Away, the Mice Will Play.Frontiers in digital health · 2021Article
- The Influence of Cognitive Biases and Financial Factors on Forecast Accuracy of Analysts.Frontiers in psychology · 2021Article
- Alliance Ruptures and Resolutions in Personality Disorders.Current psychiatry reports · 2020Review
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Authors and funding
6 authors.
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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.
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