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Second, since the session transcripts are unstructured data, we developed a treatment-oriented, speaker diarization ML model in accordance with their utterances in the treatment session. A team of trained graduate-level clinicians tagged over 2500 therapy conversations and labeled the speakers as either “patient” or “therapist.” These data were consequently used in a model that analyzed the full transcribed session and assigned a speaker label for each participant.
JMIR Form Res 2022;6(8):e39846
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