Application of New Qualitative Voicing Time-Frequency Features for Speaker Recognition View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2007

AUTHORS

Nidhal Ben Aloui , Hervé Glotin , Patrick Hebrard

ABSTRACT

This paper presents original and efficient Qualitative Time-Frequency (QTF) speech features for speaker recognition based on a med-term speech dynamics qualitative representation. For each frame of around 150ms, we estimate and binarize a suband voicing activity estimation of 6 frequency subands. We then derive the Allen temporal relations graph between these 6 time intervals. This set of temporal relations, estimated at each frame, feeds a neural network which is trained for speaker recognition. Experiments are conducted on fifty speakers (males and females) of a reference radio database ESTER (40 hours) with continuous speech. Our best model generates around 3% of frame class error, without using information of frame continuity, which is similar to state of the art. Moreover, our QTF generates a simple and light representation using only 15 integers for coding speaker identity. More... »

PAGES

1154-1163

References to SciGraph publications

Book

TITLE

Advances in Biometrics

ISBN

978-3-540-74548-8
978-3-540-74549-5

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-74549-5_120

DOI

http://dx.doi.org/10.1007/978-3-540-74549-5_120

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1039141695


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