@Article{JICS-6-055,
author = {Poonam Bansal, Amita Dev and Shail Bala Jain},
title = {Normalized Autocorrelation based Features for Robust Speech Recognition},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {6},
number = {1},
pages = {055--063},
abstract = { This paper presents a robust approach for an automatic speech recognition system (ASR) when 
both  additive  and  convolutional  noises  corrupt  the  speech  signal.  Robust  features  are  derived  by  assuming 
that  the  corrupting  noise  is  stationary  and  the  channel  effect  is  fixed  during  the  utterance.  In  the  proposed 
method the effect of additive and convolutional distortions are minimized by two stage filtering.  The first 
filtering  stage  includes  differential  temporal  filtering  in  the  autocorrelation  domain  for  reducing  additive 
noise  effects,  followed  by  additional  filtering  in  the  logarithmic  spectrum  domain  to  reduce  convolutional 
noise  effects.  Convolutional  channel  distortion  is  assumed  to  be  linear  and  time  invariant.  A  task  of 
multispeaker  isolated  Hindi  word  recognition  is  conducted  to  demonstrate  the  effectiveness  of  using  these 
robust  features.  The  cases  of  channel  filtered  speech  signal  corrupted  by  white  noise  and  different  colored 
noises  such  as  factory,  babble  and  F16,  which  are  further  corrupted  by  channel  distortion  are  tested.  
Experimental results show that the proposed method can significantly improve the performance of isolated 
Hindi word recognition system in noisy environment. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22696.html}
}