Vocal Tract Length Invariant Features for Automatic Speech Recognition

Alfred Mertins; Jan Rademacher

doi:10.1109/ASRU.2005.1566473

Vocal Tract Length Invariant Features for Automatic Speech Recognition

Alfred Mertins, Jan Rademacher

Institut für Signalverarbeitung

Carl von Ossietzky Universität Oldenburg

15 Zitate (Scopus)

Abstract

The effects of vocal tract length (VTL) variation are often approximated by linear frequency warping of short-time spectra. Based on this relationship, we present a method for generating vocal tract length invariant features. These new features are computed as translation invariant, correlation-type features in a log-frequency domain. In phoneme classification experiments, their discrimination capabilities turned out to be considerably better than for Mel-frequency cepstral coefficients (MFCCs). The best results are obtained when VTL-invariant (VTLI) features and MFCCs are combined. The superiority of the combined feature set and its resilience to VTL variations is also shown for word recognition, using the TIDIGITS corpus and the HTK recognizer.

Originalsprache	Englisch
Seiten	33-37
Seitenumfang	5
DOIs	https://doi.org/10.1109/ASRU.2005.1566473
Publikationsstatus	Veröffentlicht - 01.12.2005
Veranstaltung	2005 IEEE Automatic Speech Recognition and Understanding Workshop - Cancun, Mexico Dauer: 27.11.2005 → 01.12.2005 Konferenznummer: 68918

Tagung, Konferenz, Kongress

Tagung, Konferenz, Kongress	2005 IEEE Automatic Speech Recognition and Understanding Workshop
Kurztitel	ASRU 2005
Land/Gebiet	Mexico
Ort	Cancun
Zeitraum	27.11.05 → 01.12.05

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10.1109/ASRU.2005.1566473

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https://www.scopus.com/pages/publications/33846210086

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AU - Rademacher, Jan

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N2 - The effects of vocal tract length (VTL) variation are often approximated by linear frequency warping of short-time spectra. Based on this relationship, we present a method for generating vocal tract length invariant features. These new features are computed as translation invariant, correlation-type features in a log-frequency domain. In phoneme classification experiments, their discrimination capabilities turned out to be considerably better than for Mel-frequency cepstral coefficients (MFCCs). The best results are obtained when VTL-invariant (VTLI) features and MFCCs are combined. The superiority of the combined feature set and its resilience to VTL variations is also shown for word recognition, using the TIDIGITS corpus and the HTK recognizer.

AB - The effects of vocal tract length (VTL) variation are often approximated by linear frequency warping of short-time spectra. Based on this relationship, we present a method for generating vocal tract length invariant features. These new features are computed as translation invariant, correlation-type features in a log-frequency domain. In phoneme classification experiments, their discrimination capabilities turned out to be considerably better than for Mel-frequency cepstral coefficients (MFCCs). The best results are obtained when VTL-invariant (VTLI) features and MFCCs are combined. The superiority of the combined feature set and its resilience to VTL variations is also shown for word recognition, using the TIDIGITS corpus and the HTK recognizer.

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M3 - Conference Papers

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T2 - 2005 IEEE Automatic Speech Recognition and Understanding Workshop

Y2 - 27 November 2005 through 1 December 2005

ER -

Vocal Tract Length Invariant Features for Automatic Speech Recognition

Abstract

Tagung, Konferenz, Kongress

UN SDGs

Dokumente

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