Considering speech quality in speaker verification fusion

Yosef A. Solewicz; Moshe Koppel

Considering speech quality in speaker verification fusion

Yosef A. Solewicz, Moshe Koppel

Department of Computer Science

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

This paper emphasizes the benefits of embedding data categorization within fusion of classifiers for text-independent speaker verification. A selective fusion framework is presented which considers data idiosyncrasies by assigning particular test samples to appropriate fusion schemes. As an extension, incompatible data can be spotted and excluded from inherent classification errors. In addition, it's shown that multi-resolution low-level classifiers successfully boost fusion capabilities in noise.

Original language	English
Pages	2189-2192
Number of pages	4
State	Published - 2005
Event	9th European Conference on Speech Communication and Technology - Lisbon, Portugal Duration: 4 Sep 2005 → 8 Sep 2005

Conference

Conference	9th European Conference on Speech Communication and Technology
Country/Territory	Portugal
City	Lisbon
Period	4/09/05 → 8/09/05

Cite this

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title = "Considering speech quality in speaker verification fusion",

abstract = "This paper emphasizes the benefits of embedding data categorization within fusion of classifiers for text-independent speaker verification. A selective fusion framework is presented which considers data idiosyncrasies by assigning particular test samples to appropriate fusion schemes. As an extension, incompatible data can be spotted and excluded from inherent classification errors. In addition, it's shown that multi-resolution low-level classifiers successfully boost fusion capabilities in noise.",

author = "Solewicz, {Yosef A.} and Moshe Koppel",

year = "2005",

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AU - Solewicz, Yosef A.

AU - Koppel, Moshe

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AB - This paper emphasizes the benefits of embedding data categorization within fusion of classifiers for text-independent speaker verification. A selective fusion framework is presented which considers data idiosyncrasies by assigning particular test samples to appropriate fusion schemes. As an extension, incompatible data can be spotted and excluded from inherent classification errors. In addition, it's shown that multi-resolution low-level classifiers successfully boost fusion capabilities in noise.

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T2 - 9th European Conference on Speech Communication and Technology

Y2 - 4 September 2005 through 8 September 2005

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Considering speech quality in speaker verification fusion

Abstract

Conference

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