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An Introduction to Application-Independent Evaluation of Speaker Recognition Systems
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“EER is invariant to any order-preserving transforms of the predicted MOS scores (including global scaling and shifting) (Van Leeuwen and Brümmer, 2007, § 3.1).”
From ASVspoof 5: Design, collection and validation of resources for spoofing, deepfake, and adversarial attack detection using crowdsourced speech (2026) · cites this paper as Van Leeuwen and Brümmer, 2007, § 3.1
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References 18 · Cited by 179
Research works printed but not linked: 14
- Martin, A.: Evaluations of Automatic Speaker Classification Systems. In: Müller, C. (ed.) Speaker Classification I. LNCS(LNAI), vol. 4343, Springer, Heidelberg (2007) · DOI
- Martin, A., Doddington, G., Kamm, T., Ordowski, M., Przybocki, M.: The DET curve in assessment of detection task performance. In: Proc. Eurospeech 1997, Rhodes, Greece, pp. 1895–1898 (1997) · DOI
- Brümmer, N.: Application-independent evaluation of speaker detection. In: Proc. Odyssey, Speaker and Language recognition workshop, ISCA 2004, pp. 33–40 (2004)
- Brümmer, N., du Preez, J.: Application-independent evaluation of speaker detection. Computer Speech and Language 20, 230–275 (2006) · DOI
- Campbell, W.M., Reynolds, D.A., Campbell, L.P., Brady, K.J.: Estimating and evaluating confidence for forensic speaker recognition. In: Proc. ICASSP, pp. 717–720 (2005) · DOI
- Campbell, W.M., Brady, K.J., Campbell, J.P., Granvile, R., Reynolds, D.A.: Understanding scores in forensic speaker recognition. In: Proc. Odyssey 2006 Speaker and Language Recognition Workshop (2006) · DOI
- Ramos-Castro, D., González-Rodríguez, J., Ortega-Garcia, J.: Likelihood ratio calibration in a transparent and testable forensic speaker recognition framework. In: Proc. Odyssey 2006 Speaker and Language Recognition Workshop (2006) · DOI
- Brümmer, N., van Leeuwen, D.A.: On calibration of language recognition scores. In: Proc. Odyssey 2006 Speaker and Language recognition workshop (2006) · DOI
- Auckenthaler, R., Carey, M., Lloyd-Thomas, H.: Score normalization for text-independetn speaker verification systems. Digital Signal Processing 10, 42–54 (2000) · DOI
- Navrátil, J., Ramsawamy, G.N.: The awe and mistery of t-norm. In: Proc. Eurospeech, pp. 2009–2012 (2003) · DOI
- Van Leeuwen, D.A., Martin, A.F., Przybocki, M.A., Bouten, J.S.: NIST and TNO-NFI evaluations of automatic speaker recognition. Computer Speech and Language 20, 128–158 (2006) · DOI
- Swets, J.A.: Signal detection and recognition by human observers; contemporary readings. Wiley, New York (1964)
- Green, D.M., Swets, J.A.: Signal Detection Theory and Psychophysics. Wiley, New York (1966)
- Doddington, G.R., Przybocki, M.A., Martin, A.F., Reynolds, D.A.: The NIST speaker recognition evaluation—Overview, methodology, systems, results, perspective. Speech Communication 31, 225–254 (2000) · DOI
Metadata
- Authors
- van Leeuwen, D. A., & Brümmer, N.
- Affiliations
- Netherlands Organisation for Applied Scientific Research (NL)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2007
- Pages
- 330-353
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1481604723
- Cited by
- 179
- References
- 18
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 7 · 2023: 8 · 2022: 13 · 2021: 18 · 2020: 16 · 2019: 9 · 2018: 4 · 2017: 13 · 2016: 13 · 2015: 8 · 2014: 11 · 2013: 12 · 2012: 8
- FWCI
- 4.55
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- TNO Repository (submittedVersion)
- Repository hosted by TU Delft Library (TU Delft) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Keywords
- score calibration, speaker detection, NIST Speaker Recognition Evaluation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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