The DOI matches its Crossref record.
Eigenvoice modeling with sparse training data
Abstract
We derive an exact solution to the problem of maximum likelihood estimation of the supervector covariance matrix used in extended MAP (or EMAP) speaker adaptation and show how it can be regarded as a new method of eigenvoice estimation. Unlike other approaches to the problem of estimating eigenvoices in situations where speaker-dependent training is not feasible, our method enables us to estimate as many eigenvoices from a given training set as there are training speakers. In the limit as the amount of training data for each speaker tends to infinity, it is equivalent to cluster adaptive training.
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
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-citationSupport vector machines using GMM supervectors for speaker verification
Campbell, W. M., Sturim, D. E., & Reynolds, D. A. (2006) · IEEE Signal Processing Letters · doi:10.1109/lsp.2006.870086 · Cited by 1,013
Joint Factor Analysis Versus Eigenchannels in Speaker Recognition
Kenny, P., Boulianne, G., Ouellet, P., & Dumouchel, P. (2007) · IEEE Transactions on Audio, Speech and Language Processing · doi:10.1109/tasl.2006.881693 · Cited by 695
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
Automatic speech recognition and speech variability: A review
Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., Fissore, L., Laface, P., Mertins, A., Ris, C., et al. (2007) · Speech Communication · doi:10.1016/j.specom.2007.02.006 · Cited by 529
Metadata
- Authors
- Kenny, P., Boulianne, G., & Dumouchel, P.
- Venue
- IEEE Transactions on Speech and Audio Processing
- Published
- 2005-05
- Volume
- 13
- Issue
- 3
- Pages
- 345-354
- ISSN
- 1063-6676 (ISSN-L)
- OpenAlex
- W2121415728
- Cited by
- 475
- References
- 0
- FWCI
- 13.74
- 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: Eigenvoice modeling with sparse training data. https://app.cochat.ai/papers/10.1109/tsa.2004.840940
- The paper
- Kenny, P., Boulianne, G., & Dumouchel, P. (2005). Eigenvoice modeling with sparse training data. IEEE Transactions on Speech and Audio Processing. https://doi.org/10.1109/tsa.2004.840940
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.