Blind Normalization of Speech From Different Channels and Speakers

dc.creatorLevin, David N.
dc.date2002-04-02
dc.date.accessioned2026-07-07T03:18:14Z
dc.date.available2026-07-07T03:18:14Z
dc.descriptionThis paper describes representations of time-dependent signals that are invariant under any invertible time-independent transformation of the signal time series. Such a representation is created by rescaling the signal in a non-linear dynamic manner that is determined by recently encountered signal levels. This technique may make it possible to normalize signals that are related by channel-dependent and speaker-dependent transformations, without having to characterize the form of the signal transformations, which remain unknown. The technique is illustrated by applying it to the time-dependent spectra of speech that has been filtered to simulate the effects of different channels. The experimental results show that the rescaled speech representations are largely normalized (i.e., channel-independent), despite the channel-dependence of the raw (unrescaled) speech.
dc.description4 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/cs/0204003
dc.identifierhttp://arxiv.org/abs/cs/0204003
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31035
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleBlind Normalization of Speech From Different Channels and Speakers
dc.typetext

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