Ontology type: schema:Chapter Open Access: True
2007
AUTHORSNidhal Ben Aloui , Hervé Glotin , Patrick Hebrard
ABSTRACTThis 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... »
PAGES1154-1163
Advances in Biometrics
ISBN
978-3-540-74548-8
978-3-540-74549-5
http://scigraph.springernature.com/pub.10.1007/978-3-540-74549-5_120
DOIhttp://dx.doi.org/10.1007/978-3-540-74549-5_120
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