TIME-FREQUENCY PRINCIPAL COMPONENTS OF SPEECH: APPLICATION TO SPEAKER IDENTIFICATION

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TIME-FREQUENCY PRINCIPAL COMPONENTS OF SPEECH
APPLICATION TO SPEAKER IDENTIFICATION

• Ivan Magrin-Chagnolleau and Geoffrey Durou

Rice University, Houston, Texas - Faculté
Polytechnique de Mons, Mons, Belgiumivan_at_ieee.org
- durou_at_tcts.fpms.ac.be
Introduction
Principle of the Vector Filtering of Spectral
Trajectories
? Goal filtering of the spectral vectors in
order to extract dynamic speaker characteristic
information. ? Data-driven approach the
filtering is learned on the training data. ?
Class-dependent filtering one filtering for each
speaker. ? Principle principal component
analysis applied to spectral vectors augmented by
their context ? Time-Frequency Principal
Components (TFPC)
Experiments
? Task closed-set text-independent speaker
identification. ? Database subset of the
POLYCOST database - 112 speakers (64 females and
48 males) - 90 seconds of training (free text
through the telephone) - 560 test utterances of 5
second in average. ? Spectrum 13 Mel-scale
filterbank coefficients. ? Cepstrum 12 cepstral
coefficients (the first one is discarded)
augmented by their ? parameters. ? TFPC
Filtering 1 TFPC filtering for each speaker
using several sizes of context - 1 TFPC for all
the speakers. ? Modeling Gaussian mixture models
with 8 components and diagonal covariance
matrices.