Speaker diarisation and longitudinal linking in multi-genre broadcast data

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Karanasou, P 
Gales, MJF 
Lanchantin, P 
Liu, X 
Qian, Y 

This paper presents a multi-stage speaker diarisation system with longitudinal linking developed on BBC multi-genre data for the 2015 Multi-Genre Broadcast (MGB) challenge. The basic speaker diarisation system draws on techniques from the Cambridge March 2005 system with a new deep neural network (DNN)-based speech/non speech segmenter. A newly developed linking stage is next added to the basic diarisation output aiming at the identification of speakers across multiple episodes of the same series. The longitudinal constraint imposes an incremental processing of the episodes, where speaker labels for each episode can be obtained using only material from the episode in question, and those broadcast earlier in time. The nature of the data as well as the longitudinal linking constraint position this diarisation task as a new open-research topic, and a particularly challenging one. Different linking clustering metrics are compared and the lowest within-episode and cross-episode DER scores are achieved on the MGB challenge evaluation set.

speaker diarisation, speaker segmentation, agglomerative clustering, longitudinal linking
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2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings
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2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)
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This work is in part supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology). C. Zhang is also supported by a Cambridge International Scholarship from the Cambridge Commonwealth, European & International Trust.