  Conversations between a clinician and a patient, in natural conditions, are
valuable sources of information for medical follow-up. The automatic analysis
of these dialogues could help extract new language markers and speed-up the
clinicians' reports. Yet, it is not clear which speech processing pipeline is
the most performing to detect and identify the speaker turns, especially for
individuals with speech and language disorders. Here, we proposed a split of
the data that allows conducting a comparative evaluation of speaker role
recognition and speaker enrollment methods to solve this task. We trained
end-to-end neural network architectures to adapt to each task and evaluate each
approach under the same metric. Experimental results are reported on
naturalistic clinical conversations between Neuropsychologist and Interviewees,
at different stages of Huntington's disease. We found that our Speaker Role
Recognition model gave the best performances. In addition, our study underlined
the importance of retraining models with in-domain data. Finally, we observed
that results do not depend on the demographics of the Interviewee, highlighting
the clinical relevance of our methods.
