Speaker diarization of meetings based on speaker role n-gram models

Speaker diarization of meeting recordings is generally based on acoustic information ignoring that meetings are instances of conversations. Several recent works have shown that the sequence of speakers in a conversation and their roles are related and statistically predictable. This paper proposes the use of speaker roles n-gram model to capture the conversation patterns probability and investigates its use as prior information into a state-of-the-art diarization system. Experiments are run on the AMI corpus annotated in terms of roles. The proposed technique reduces the diarization speaker error by 19% when the roles are known and by 17% when they are estimated. Furthermore the paper investigates how the n-gram models generalize to different settings like those from the Rich Transcription campaigns. Experiments on 17 meetings reveal that the speaker error can be reduced by 12% also in this case thus the n-gram can generalize across corpora.