Intra-speaker variability compensation in speaker verification with limited enrolling data

2006 ◽  
Author(s):  
Claudio Garreton ◽  
Nestor Becerra Yoma ◽  
Carlos Molina ◽  
Fernando Huenupan
Keyword(s):  
Speaker Verification ◽  
Speaker Variability ◽  
Variability Compensation

Improving speaker verification performance against long-term speaker variability

2016 ◽  
Vol 79 ◽  
pp. 14-29 ◽  
Author(s):  
Linlin Wang ◽  
Jun Wang ◽  
Lantian Li ◽  
Thomas Fang Zheng ◽  
Frank K. Soong
Keyword(s):  
Speaker Verification ◽  
Speaker Variability

Speaker Verification with Fuzzy Fusion and Genetic Optimization

1999 ◽  
Vol 3 (6) ◽  
pp. 451-456
Author(s):  
Tuan Pham ◽  
◽  
Michael Wagner ◽  
Keyword(s):  
Genetic Algorithms ◽  
Speaker Verification ◽  
A Priori ◽  
Similarity Measures ◽  
Error Rates ◽  
Fuzzy Integral ◽  
Speech Corpus ◽  
Speaker Variability ◽  
Verification System ◽  
Fuzzy Fusion

Most speaker verification systems are based on similarity or likelihood normalization techniques as they help to better cope with speaker variability. In the conventional normalization, the it a priori probabilities of the cohort speakers are assumed to be equal. From this standpoint, we apply the fuzzy integral and genetic algorithms to combine the likelihood values of the cohort speakers in which the assumption of equal <I>a priori</I> probabilities is relaxed. This approach replaces the conventional normalization term by the fuzzy integral which acts as a non-linear fusion of the similarity measures of an utterance assigned to the cohort speakers. Furthermore, genetic algorithms are applied to find optimal fuzzy densities which are very important for the fuzzy fusion. We illustrate the performance of the proposed approach by testing the speaker verification system with both the conventional and the proposed algorithms using the commercial speech corpus TI46. The results in terms of the equal error rates show that the speaker verification system using the fuzzy integral is more favorable than the conventional normalization method.

scholarly journals An analysis of speaker dependent models in replay detection

2020 ◽  
Vol 9 ◽  
Author(s):  
Gajan Suthokumar ◽  
Kaavya Sriskandaraja ◽  
Vidhyasaharan Sethu ◽  
Eliathamby Ambikairajah ◽  
Haizhou Li
Keyword(s):  
Speaker Verification ◽  
Detection System ◽  
Gaussian Mixture ◽  
Equal Error Rate ◽  
Speaker Variability ◽  
Speaker Independent ◽  
Spoofing Detection ◽  
Potential Benefits ◽  
Target Speaker ◽  
Small Improvement

Most research on replay detection has focused on developing a stand-alone countermeasure that runs independently of a speaker verification system by training a single spoofed model and a single genuine model for all speakers. In this paper, we explore the potential benefits of adapting the back-end of a spoofing detection system towards the claimed target speaker. Specifically, we characterize and quantify speaker variability by comparing speaker-dependent and speaker-independent (SI) models of feature distributions for both genuine and spoofed speech. Following this, we develop an approach for implementing speaker-dependent spoofing detection using a Gaussian mixture model (GMM) back-end, where both the genuine and spoofed models are adapted to the claimed speaker. Finally, we also develop and evaluate a speaker-specific neural network-based spoofing detection system in addition to the GMM based back-end. Evaluations of the proposed approaches on replay corpora BTAS2016 and ASVspoof2017 v2.0 reveal that the proposed speaker-dependent spoofing detection outperforms equivalent SI replay detection baselines on both datasets. Our experimental results show that the use of speaker-specific genuine models leads to a significant improvement (around 4% in terms of equal error rate (EER)) as previously shown and the addition of speaker-specific spoofed models adds a small improvement on top (less than 1% in terms of EER).

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