Paramétrisation de la Parole basée sur une Modélisation des Filtres Cochléaires: Application au RAP
Zied Hajaiej, Kaïs Ouni, Noureddine Ellouze.
Signal processing front end for extracting the feature
set is an important stage in any speech recognition
system. The optimum feature set is still not yet
decided. There are many types of features, which are
derived differently and have good impact on the
recognition rate. This paper presents one more
successful technique to extract the feature set from a
speech signal, which can be used in speech recognition
systems. Our technique based on the human auditory
system characteristics and relies on the gammachirp
filterbank to emulate asymmetric frequency response
and level dependent frequency response. For
evaluation a comparative study was operated with
standard MFCC and PLP.