Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 475

The DOI matches its Crossref record.

Eigenvoice modeling with sparse training data

Kenny, P., Boulianne, G., Dumouchel, P.·2005·IEEE Transactions on Speech and Audio Processing

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/tsa.2004.840940

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.

A few free checks a day · papers already verified open instantly · results are public

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

See all 475

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.