  The task of determining a speaker's native language based only on his
speeches in a second language is known as Native Language Identification or
NLI. Due to its increasing applications in various domains of speech signal
processing, this has emerged as an important research area in recent times. In
this paper we have proposed an i-vector based approach to develop an automatic
NLI system using MFCC and GFCC features. For evaluation of our approach, we
have tested our framework on the 2016 ComParE Native language sub-challenge
dataset which has English language speakers from 11 different native language
backgrounds. Our proposed method outperforms the baseline system with an
improvement in accuracy by 21.95% for the MFCC feature based i-vector framework
and 22.81% for the GFCC feature based i-vector framework.
