alexa Improvement of the Speaker Verification System with Fe
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article

Improvement of the Speaker Verification System with Feature Level and Score Level Normalization Techniques

Kshirod Sarmah1, Utpal Bhattacharjee2
  1. Research Scholar, Department of Computer Science and Engineering, Rajiv Gandhi University, Rono Hills, Doimukh, Arunachal Pradesh, India
  2. Associate Professor, Department of Computer Science and Engineering, Rajiv Gandhi University, Rono Hills, Doimukh, Arunachal Pradesh, India
Related article at Pubmed, Scholar Google


The performance of a text independent Speaker verification (SV) system has degraded when speaker model training is done in one environment while the testing is done in another, due to mismatching of phonetic contents of speech utterances, recording environment, session variability and sensor variability of training and testing criteria, which are major problems in speaker verification system. The robustness of SV system has been improved by applying different Voice Activity Detection (VAD) techniques like Cepstral Mean Normalization (CMN), Cepstral Variance Normalization (CVN) techniques in features level and score normalization techniques in score level. In this paper we report the experiment carried out on the recently collected speaker recognition database Arunachali Language Speech Database (ALS-DB). The collected database is evaluated with Gaussian mixture model and Universal Background Model (GMM-UBM) and Mel- Frequency Cepstral Coefficients (MFCC) with its first and second order derivatives as well as Prosodic features as a front end feature vectors. The performance of the speaker verification system has been improved by applying CVN at the feature level as well as score normalization technique Test-normalization (T-Norm) in the score level. And also we observe that the performance of SV system vastly improved while applying CVN in feature level and T-Norm in score level at the same time. We observe that combining MFCC with Prosodic features improved the performance of the SV system with 7.08%, while T-Norm improved the SV system with 3.22% and CVN has improved with 3.90%.


Share This Page

Additional Info

Loading Please wait..
Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version