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 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.-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　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.