Data-driven clustering for blind feature mapping in speaker verification.
作者： Michael W. Mason , Brendan J. Baker , Robert J. Vogt , Sridha Sridharan
DOI:
关键词: Speaker diarisation 、 Computer science 、 Pattern recognition 、 NIST 、 Context model 、 Data-driven 、 Channel (digital image) 、 Cluster analysis 、 Artificial intelligence 、 Speaker recognition
摘要: Handset and channel mismatch degrades the performance of automatic speaker recognition systems significantly. This paper enhances feature mapping technique by proposing an iterative clustering approach to context model generation which offers improvement in trained on labelled data potential train absence correctly background data. The clustered models is demonstrated expanded version NIST 2003 Extended Data Task (EDT) protocol.
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