標題: Fast scoring for PLDA with uncertainty propagation via i-vector grouping
作者: Lin, Wei-wei
Mak, Man-Wai
Chien, Jen-Tzung
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: Speaker verification;i-Vector/PLDA;Uncertainty Propagation;Duration mismatch
公開日期: 1-Sep-2017
摘要: The i-vector/PLDA framework has gained huge popularity in text-independent speaker verification. This approach, however, lacks the ability to represent the reliability of i-vectors. As a result, the framework performs poorly when presented with utterances of arbitrary duration. To address this problem, a method called uncertainty propagation (UP) was proposed to explicitly model the reliability of an i-vector by an utterance-dependent loading matrix. However, the utterance-dependent matrix greatly complicates the evaluation of likelihood scores. As a result, PLDA with UP, or PLDA-UP in short, is far more computational intensive than the conventional PLDA. In this paper, we propose to group i-vectors with similar reliability, and for each group the utterance-dependent loading matrices are replaced by a representative one. This arrangement allows us to pre-compute a set of representative matrices that cover all possible i-vectors, thereby greatly reducing the computational cost of PLDA-UP while preserving its ability in discriminating the reliability of i-vectors. Experiments on NIST 2012 SRE show that the proposed method can perform as good as the PLDA with UP while the scoring time is only 3.18% of it. (C) 2017 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.csl.2017.02.009
http://hdl.handle.net/11536/145647
ISSN: 0885-2308
DOI: 10.1016/j.csl.2017.02.009
期刊: COMPUTER SPEECH AND LANGUAGE
Volume: 45
起始頁: 503
結束頁: 515
Appears in Collections:Articles