The DOI matches its Crossref record.
Towards fully Bayesian speaker recognition: integrating out the between-speaker covariance
Abstract
We propose a variational Bayes solution to integrate out the model parameters in a generative i-vector speaker recognizer. The existing state-of-the-art in generative i-vector modelling plugs in fixed maximum-likelihood point-estimates of model parameters. This recipe may suffer from over-fitting of especially the between-speaker covariance. We show how to integrate out the between-speaker covariance and demonstrate dramatic improvements on NIST SRE 2010.
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“A problem that could arise when calibrating [46,47] is data scarcity.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [46]
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“The references indicate that the use of simulated data gives a big improvement in data scarcity situations, but the testing of the validity of simulated databases for the operational use of systems in a real setup is still controversial.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [46]
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Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
Rodriguez, A. M., Geradts, Z., & Worring, M. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111239 · Cited by 19
Metadata
- Authors
- Villalba, J., & Brümmer, N.
- Venue
- Interspeech 2011
- Publisher
- ISCA
- Published
- 2011-08-27
- Pages
- 505-508
- Cited by
- 26
- References
- 0
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