The spectral parameters that result from filtering the
frequency sequence of log mel-scaled filter-bank energies
with a simple first or second order FIR filter have proved
to be an efficient speech representation in terms of both
speech recognition rate and computational load. Recently,
the authors have shown that this frequency filtering can
approximately equalize the cepstrum variance enhancing
the oscillations of the spectral envelope curve that are
most effective for discrimination between speakers. Even
better speaker identification results than using melcepstrum
have been obtained on the TIMIT database,
especially when white noise was added. On the other
hand, the hybridization of both linear prediction and
filter-bank spectral analysis using either cepstral
transformation or the alternative frequency filtering has
been explored for speaker verification. The combination
of hybrid spectral analysis and frequency filtering, that
had shown to be able to outperform the conventional
techniques in clean and noisy word recognition, has yield
good text-dependent speaker verification results on the
new speaker-oriented telephone-line POLYCOST
database.Peer ReviewedPostprint (published version
