Fast scoring for PLDA with uncertainty propagation via i-vector grouping. (September 2017)

• Record Type: Journal Article
• Authors: Lin, Wei-wei; Mak, Man-Wai; Chien, Jen-Tzung
• Is Part Of: Computer speech & language. Volume 45(2017)
• Page Start: 503
• Page End: 515
• Publication Date: 2017-09
• Subjects: Speaker verification -- i-Vector/PLDA -- Uncertainty Propagation -- Duration mismatch; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.02.009
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: Grouping i-vectors based on their reliability for finding representative posterior covariance matrices. A fast scoring scheme for PLDA with uncertainty propagation. Speed up uncertainty propagation in i-vector/PLDA by 31 times without losing performance. Abstract: 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.