Generalized cyclic transformations in speaker-independent speech recognition

F. Müller; E. Belilovsky; A. Mertins

doi:10.1109/ASRU.2009.5373284

Generalized cyclic transformations in speaker-independent speech recognition

F. Müller, E. Belilovsky, A. Mertins

Institut für Signalverarbeitung

Abstract

A feature extraction method is presented that is robust against vocal tract length changes. It uses the generalized cyclic transformations primarily used within the field of pattern recognition. In matching training and testing conditions the resulting accuracies are comparable to the ones of MFCCs. However, in mismatching training and testing conditions with respect to the mean vocal tract length the presented features significantly outperform the MFCCs.

Originalsprache	Englisch
Titel	2009 IEEE Workshop on Automatic Speech Recognition Understanding
Seitenumfang	5
Herausgeber (Verlag)	IEEE
Erscheinungsdatum	01.11.2009
Seiten	211-215
ISBN (Print)	978-1-4244-5478-5
ISBN (elektronisch)	978-1-4244-5479-2
DOIs	https://doi.org/10.1109/ASRU.2009.5373284
Publikationsstatus	Veröffentlicht - 01.11.2009
Veranstaltung	2009 IEEE Workshop on Automatic Speech Recognition and Understanding - Merano, Italien Dauer: 13.12.2009 → 17.12.2009 Konferenznummer: 79490

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10.1109/ASRU.2009.5373284

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Invariante Merkmale für die automatische Spracherkennung
Mertins, A. (Projektleiter*in (PI))
01.01.07 → 31.12.11
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@inproceedings{b3e1f1f69a9446fabfd7a17e81cf7657,

title = "Generalized cyclic transformations in speaker-independent speech recognition",

abstract = "A feature extraction method is presented that is robust against vocal tract length changes. It uses the generalized cyclic transformations primarily used within the field of pattern recognition. In matching training and testing conditions the resulting accuracies are comparable to the ones of MFCCs. However, in mismatching training and testing conditions with respect to the mean vocal tract length the presented features significantly outperform the MFCCs.",

author = "F. M{\"u}ller and E. Belilovsky and A. Mertins",

year = "2009",

month = nov,

day = "1",

doi = "10.1109/ASRU.2009.5373284",

language = "English",

isbn = "978-1-4244-5478-5",

series = " IEEE Workshop on Automatic Speech Recognition and Understanding",

publisher = "IEEE",

pages = "211--215",

booktitle = "2009 IEEE Workshop on Automatic Speech Recognition Understanding",

note = "2009 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2009 ; Conference date: 13-12-2009 Through 17-12-2009",

}

Müller, F, Belilovsky, E & Mertins, A 2009, Generalized cyclic transformations in speaker-independent speech recognition. in 2009 IEEE Workshop on Automatic Speech Recognition Understanding. IEEE Workshop on Automatic Speech Recognition and Understanding, IEEE, S. 211-215, 2009 IEEE Workshop on Automatic Speech Recognition and Understanding, Merano, Italien, 13.12.09. https://doi.org/10.1109/ASRU.2009.5373284

Generalized cyclic transformations in speaker-independent speech recognition. / Müller, F.; Belilovsky, E.; Mertins, A.
2009 IEEE Workshop on Automatic Speech Recognition Understanding. IEEE, 2009. S. 211-215 ( IEEE Workshop on Automatic Speech Recognition and Understanding).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Generalized cyclic transformations in speaker-independent speech recognition

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Y1 - 2009/11/1

N2 - A feature extraction method is presented that is robust against vocal tract length changes. It uses the generalized cyclic transformations primarily used within the field of pattern recognition. In matching training and testing conditions the resulting accuracies are comparable to the ones of MFCCs. However, in mismatching training and testing conditions with respect to the mean vocal tract length the presented features significantly outperform the MFCCs.

AB - A feature extraction method is presented that is robust against vocal tract length changes. It uses the generalized cyclic transformations primarily used within the field of pattern recognition. In matching training and testing conditions the resulting accuracies are comparable to the ones of MFCCs. However, in mismatching training and testing conditions with respect to the mean vocal tract length the presented features significantly outperform the MFCCs.

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DO - 10.1109/ASRU.2009.5373284

M3 - Conference contribution

SN - 978-1-4244-5478-5

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PB - IEEE

T2 - 2009 IEEE Workshop on Automatic Speech Recognition and Understanding

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ER -

Generalized cyclic transformations in speaker-independent speech recognition

Abstract

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