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 con-versations. 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 investi-gates 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. Index Terms — Speaker diarization, meeting recordings, multi-party conversations, Speaker Roles, Viterbi decoding