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An overview of text-independent speaker recognition: From features to supervectors

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Kinnunen, Tomi, Li, Haizhou·2010·Speech Communication
DOI 10.1016/j.specom.2009.08.009
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Abstract

This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.

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Metadata

Authors
Kinnunen, T., & Li, H.
Venue
Speech Communication
Publisher
Elsevier BV
Published
2010-01
Volume
52
Issue
1
Pages
12-40
ISSN
0167-6393 (ISSN-L)
DOI
10.1016/j.specom.2009.08.009
OpenAlex
W2154278880
Cited by
1,434
References
122
FWCI
65.78
Type
journal article
Language
en
Open access
green
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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.

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CoChat. (2026). Citation check: An overview of text-independent speaker recognition: From features to supervectors. https://app.cochat.ai/papers/10.1016/j.specom.2009.08.009
The paper
Kinnunen, T., & Li, H. (2010). An overview of text-independent speaker recognition: From features to supervectors. Speech Communication. https://doi.org/10.1016/j.specom.2009.08.009

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.