scholarly journals User-customized password speaker verification using multiple reference and background models

2006 ◽  
Vol 48 (9) ◽  
pp. 1200-1213 ◽  
Author(s):  
Mohamed Faouzi BenZeghiba ◽  
Hervé Bourlard
Keyword(s):  
Speaker Verification ◽  
Background Models ◽  
Multiple Reference

scholarly journals Speaker Model Clustering to Construct Background Models for Speaker Verification

2017 ◽  
Vol 42 (1) ◽  
pp. 127-135
Author(s):  
Gökay Dişken ◽  
Zekeriya Tüfekci ◽  
Ulus Çevik
Keyword(s):  
Speaker Recognition ◽  
Distance Measure ◽  
Speaker Verification ◽  
Statistical Significance ◽  
Error Rates ◽  
Vector Method ◽  
Channel Mismatch ◽  
Euclidean Distance Measure ◽  
Recognition Systems ◽  
Background Models

Abstract Conventional speaker recognition systems use the Universal Background Model (UBM) as an imposter for all speakers. In this paper, speaker models are clustered to obtain better imposter model representations for speaker verification purpose. First, a UBM is trained, and speaker models are adapted from the UBM. Then, the k-means algorithm with the Euclidean distance measure is applied to the speaker models. The speakers are divided into two, three, four, and five clusters. The resulting cluster centers are used as background models of their respective speakers. Experiments showed that the proposed method consistently produced lower Equal Error Rates (EER) than the conventional UBM approach for 3, 10, and 30 seconds long test utterances, and also for channel mismatch conditions. The proposed method is also compared with the i-vector approach. The three-cluster model achieved the best performance with a 12.4% relative EER reduction in average, compared to the i-vector method. Statistical significance of the results are also given.

scholarly journals Towards an Optimal Speaker Modeling in Speaker Verification Systems using Personalized Background Models

2017 ◽  
Vol 7 (6) ◽  
pp. 3655
Author(s):  
Ayoub Bouziane ◽  
Jamal Kharroubi ◽  
Arsalane Zarghili
Keyword(s):  
Speaker Recognition ◽  
Speaker Verification ◽  
Computational Cost ◽  
Verification Task ◽  
Speech Data ◽  
Recognition Systems ◽  
Verification Systems ◽  
Background Models ◽  
Target Speaker ◽  
Speaker Modeling

<p>This paper presents a novel speaker modeling approachfor speaker recognition systems. The basic idea of this approach consists of deriving the target speaker model from a personalized background model, composed only of the UBM Gaussian components which are really present in the speech of the target speaker. The motivation behind the derivation of speakers’ models from personalized background models is to exploit the observeddifference insome acoustic-classes between speakers, in order to improve the performance of speaker recognition systems.</p>The proposed approach was evaluatedfor speaker verification task using various amounts of training and testing speech data. The experimental results showed that the proposed approach is efficientin termsof both verification performance and computational cost during the testing phase of the system, compared to the traditional UBM based speaker recognition systems.

Multiple reference points in pay satisfaction assessments

1997 ◽  
Author(s):  
Lisa Ordonez ◽  
Terry Connolly ◽  
Richard Coughlan
Keyword(s):  
Reference Points ◽  
Pay Satisfaction ◽  
Multiple Reference

Speaker Verification with Model-based and Score-based Unsupervised Adaptation Meth

2009 ◽  
Vol 35 (3) ◽  
pp. 267-271
Author(s):  
Er-Yu WANG ◽  
Wu GUO ◽  
Yi-Jie LI ◽  
Li-Rong DAI ◽  
Ren-Hua WANG
Keyword(s):  
Speaker Verification ◽  
Model Based
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