The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition
Abstract
We compare two approaches to the problem of session variability in Gaussian mixture model (GMM)-based speaker verification, eigenchannels, and joint factor analysis, on the National Institute of Standards and Technology (NIST) 2005 speaker recognition evaluation data. We show how the two approaches can be implemented using essentially the same software at all stages except for the enrollment of target speakers. We demonstrate the effectiveness of zt-norm score normalization and a new decision criterion for speaker recognition which can handle large numbers of t-norm speakers and large numbers of speaker factors at little computational cost. We found that factor analysis was far more effective than eigenchannel modeling. The best result we obtained was a detection cost of 0.016 on the core condition (all trials) of the evaluation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Speaker Verification Using Adapted Gaussian Mixture Models
Reynolds, D. A., Quatieri, T. F., & Dunn, R. B. (2000) · Digital Signal Processing · doi:10.1006/dspr.1999.0361 · Cited by 4,268
Mixtures of Probabilistic Principal Component Analyzers
Tipping, M. E., & Bishop, C. M. (1999) · Neural Computation · doi:10.1162/089976699300016728 · Cited by 1,873
Feature Warping for Robust Speaker Verification
Pelecanos, J. W., & Sridharan, S. (2001) · QUT ePrints (Queensland University of Technology) · Cited by 616
EM Algorithms for ML Factor Analysis
Rubin, D. B., & Thayer, D. T. (1982) · Psychometrika · doi:10.1007/bf02293851 · Cited by 603
Eigenvoice modeling with sparse training data
Kenny, P., Boulianne, G., & Dumouchel, P. (2005) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/tsa.2004.840940 · Cited by 475
Self-citationJoint Factor Analysis of Speaker and Session Variability: Theory and Algorithms
Kenny, P. J. (2006) · Cited by 376
Self-citationAdvances In Channel Compensation For SVM Speaker Recognition
Solomonoff, A., Campbell, W. M., & Boardman, I. (2006) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2005.1415192 · Cited by 241
Channel robust speaker verification via feature mapping
Reynolds, D. A. (2003) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2003.1202292 · Cited by 234
Speaker and Session Variability in GMM-Based Speaker Verification
Kenny, P., Boulianne, G., Ouellet, P., & Dumouchel, P. (2007) · IEEE Transactions on Audio, Speech and Language Processing · doi:10.1109/tasl.2007.894527 · Cited by 224
Self-citationThe NIST speaker recognition evaluation program
Martin, A., Przybocki, M., & Campbell, J. P. (2005) · Biometric Systems · doi:10.1007/1-84628-064-8_8 · Cited by 200
Cited by
Front-End Factor Analysis for Speaker Verification
Dehak, N., Kenny, P. J., Dehak, R., Dumouchel, P., & Ouellet, P. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2064307 · Cited by 3,549
Self-citationSpeaker Recognition by Machines and Humans: A tutorial review
Hansen, J. H. L., & Hasan, T. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2462851 · Cited by 621
Speaker recognition based on deep learning: An overview
Bai, Z., & Zhang, X.-L. (2021) · Neural Networks · doi:10.1016/j.neunet.2021.03.004 · Cited by 386
The reddots data collection for speaker recognition
Lee, K. A., Larcher, A., Wang, G., Kenny, P., Brümmer, N., Leeuwen, D. V., Aronowitz, H., Kockmann, M., Vaquero, C., Ma, B., et al. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-95 · Cited by 136
Self-citationTowards fully Bayesian speaker recognition: integrating out the between-speaker covariance
Villalba, J., & Brümmer, N. (2011) · Interspeech 2011 · doi:10.21437/interspeech.2011-142 · Cited by 26
Metadata
- Authors
- Kenny, P., Boulianne, G., Ouellet, P., & Dumouchel, P.
- Venue
- IEEE Transactions on Audio, Speech and Language Processing
- Published
- 2007-05
- Volume
- 15
- Issue
- 4
- Pages
- 1435-1447
- ISSN
- 1558-7916 (ISSN-L)
- OpenAlex
- W2107638917
- Cited by
- 695
- References
- 12
- FWCI
- 26.79
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Joint Factor Analysis Versus Eigenchannels in Speaker Recognition. https://app.cochat.ai/papers/10.1109/tasl.2006.881693
- The paper
- Kenny, P., Boulianne, G., Ouellet, P., & Dumouchel, P. (2007). Joint Factor Analysis Versus Eigenchannels in Speaker Recognition. IEEE Transactions on Audio, Speech and Language Processing. https://doi.org/10.1109/tasl.2006.881693
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
