Voice biometrics using linear Gaussian model
2014
This study introduces a linear Gaussian model-based framework for voice biometrics. The model works with discrete-time linear dynamical systems. The study motivation is to use the linear Gaussian modelling method in voice biometrics, and show that the accuracy offered by the linear Gaussian modelling method is comparable with other state-of-the-art methods such as Probabilistic Linear Discriminant Analysis and two-covariance model. An expectation-maximisation algorithm is derived to train the model and a Bayesian solution is used to calculate the log-likelihood ratio score of all trials of speakers. This approach performed well on the core-extended conditions of the NIST 2010 Speaker Recognition Evaluation, and is competitive compared with the Gaussian probabilistic linear discriminant analysis, in terms of normalised decision cost function.
Keywords:
- Gaussian network model
- Linear dynamical system
- NIST
- Speaker recognition
- Gaussian
- Probabilistic logic
- Linear discriminant analysis
- Computer science
- Pattern recognition
- Machine learning
- Bayesian probability
- Artificial intelligence
- bayesian solution
- probabilistic linear discriminant analysis
- gaussian probabilistic linear discriminant analysis
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