NDLI: Towards improving the performance of text/language independent speaker recognition systems

Content Provider	IEEE Xplore Digital Library
Author	George, K.K. Arunraj, K. Sreekumar, K.T. Kumar, C.S. Ramachandran, K.I.
Copyright Year	2014
Description	Author affiliation: Machine Intell. Res. Lab., Amrita Vishwa Vidyapeetham, Coimbatore, India (George, K.K.; Arunraj, K.; Sreekumar, K.T.; Kumar, C.S.; Ramachandran, K.I.)
Abstract	Speaker Recognition is an active area of research for the last few decades for its applications in several national security, and other forensic applications. In this work, we present the details of a speaker recognition system developed using universal background model and support vector machines(UBM-SVM). We explored several techniques to improve the performance of the baseline system developed using mel frequency cepstral coefficients(MFCC) as input features. We developed and tested the speaker recognition system for 200 speakers, using the data collected over 13 different channels, such as handset regular phone, speaker phone, regular phone headphone, regular phone, etc. We experimented with the use of RelAtive SpecTrA (RASTA) processing, and feature warping on the input MFCC features, and nuisance attribute projection (NAP) on the Gaussian mixture model supervectors derived in the system. It was seen that these techniques have helped improve the system performance significantly by minimizing the effect of different channels on the system performance. The details of the system implementation and results are presented in this paper. The complete system is developed in MATLAB and C/C++.
Starting Page	1
Ending Page	6
File Size	805424
Page Count	6
File Format	PDF
e-ISBN	9781479936120
DOI	10.1109/EPSCICON.2014.6887506
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Speaker recognition Support vector machine Mel frequency cepstral coefficient Nuisance attribute projection Gaussian mixture model RASTA filtering Support vector machines Feature Warping Training data VAD Speech Feature extraction Speaker Recognition System Maximum a Posteriori(MAP) Adaptation
Content Type	Text
Resource Type	Article

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