  Speaker clustering is an essential step in conventional speaker diarization
systems and is typically addressed as an audio-only speech processing task. The
language used by the participants in a conversation, however, carries
additional information that can help improve the clustering performance. This
is especially true in conversational interactions, such as business meetings,
interviews, and lectures, where specific roles assumed by interlocutors
(manager, client, teacher, etc.) are often associated with distinguishable
linguistic patterns. In this paper we propose to employ a supervised text-based
model to extract speaker roles and then use this information to guide an
audio-based spectral clustering step by imposing must-link and cannot-link
constraints between segments. The proposed method is applied on two different
domains, namely on medical interactions and on podcast episodes, and is shown
to yield improved results when compared to the audio-only approach.
