TY - JOUR
T1 - Novel Feature Vector Set Extraction using Spectral Peaks in Autocorrelation Domain
AU - Poonam Bansal, Amita Dev, Shail Bala Jain
JO - Journal of Information and Computing Science
VL - 2
SP - 131
EP - 141
PY - 2024
DA - 2024/01
SN - 4
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22755.html
KW - Autocorrelation domain, Feature vector set, spectral peaks 

AB -  This  paper  presents  a  new  feature  vector  set  for  noisy  speech  recognition  in  autocorrelation 
domain. The autocorrelation domain is well known for its pole preserving and noise separation properties. In 
this paper we will use the autocorrelation domain as an appropriate candidate for robust feature extraction. In 
our approach, extraction of mel frequency cepstral coefficients (MFCC) of the speech signals are proposed 
based  on  novel  Differentiated  Relative  Higher  Order  Autocorrelation  Coefficient  Sequence  Spectrum 
(DRHOASS).  In  this  approach,  initially  the  lower  lags  of  the  noisy  speech  autocorrelation  sequence  are 
discarded and then, the effect of noise is further suppressed using a high pass filter in autocorrelation domain. 
Finally,  the  feature  vector  set  of  the  speech  signal  is  found  using  the  spectral  peaks  of  the  filtered 
autocorrelation  sequence.  We  tested  our  features  on  the  Hindi  isolated-word  task  and  found  that  it  led  to 
noticeable improvements over other autocorrelation-based and differential spetral-based methods.