2018/11/09 by Ahmed Nazim Uddin, Ashequr Rahman, Md Ashequr Rahman +6
Computer Science · Engineering · Mathematics · #Artificial intelligence #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #Computer science #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Feature extraction #Feature vector #Identification (biology) #Language identification #Linguistics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mel-frequency cepstrum #Music and Audio Processing #Natural language #Natural language processing #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #Speech recognition #Support vector machine #Task (project management) #cs.CL #cs.LG #cs.SD #eess.AS #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.1811.05540
arxiv created 2018/11/09 · openalex publication_date 2018/11/09 · arxiv updated 2018/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.