چکیده (انگلیسی):

The paper presents a feature extraction method, named as
Normalized Gammachirp Cepstral Coefficients (NGCC) that incorporates
the properties of the peripheral auditory system to improve robustness in
noisy speech recognition. The proposed method is based on a second order
low-pass filter and normalized gammachirp filterbank to emulate the
mechanisms performed in the outer/middle ear and cochlea. The speech
recognition performance of this method is conducted on the speech signals
in real-world noisy environments. Experimental results demonstrate that
method outperformed the classical feature extraction methods in terms of
speech recognition rate. The used Hidden Markov Models based speech
recognition system is employed on the HTK 3.4.1 platform (Hidden
Markov Model Toolkit).