Robust Speaker Recognition using Enhanced Spectrogram

Authors

  • Sukhvinder Kaur  I.K. Gujral PTU, Jalandhar, Kapurthala, India
  • J. S. Sohal  Director, LCET, Ludhiana, Punjab, India

Keywords:

Bayesian Information Criteria, Kullback Leibler Distance Metric, Enhanced Spectrogram, Non-Linear Energy Operator, T-Test Wavelet Transform

Abstract

The aim of this paper is to present an efficient, fast and optimized system that identify the speaker in automatic speaker recognition system (ASR). It can be used in voice biometrics. In this proposed technique, the daubechies wavelet transform is used to compress the audio stream in the ratio of 1:4 with 99% of energy; their features are extracted by enhanced spectrogram with non-linear energy operator. Finally, three different distance matrices: T-test, deltaBIC and KL2 were used for feature matching of different speakers. The proposed technique using enhanced spectrogram with t-test distance metric gives fast and better results as compared to delta BIC and KL2.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-08-31

Issue

Volume 2, Issue 4, July-August-2017

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sukhvinder Kaur, J. S. Sohal, " Robust Speaker Recognition using Enhanced Spectrogram, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 4, pp.637-640, July-August-2017.