Comparison of Techniques with Speech Enhancement and Nonlinear Rectification for Robust Speaker Identificatio

Harry Anacleto Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Automatic speaker recognition is about the identification of a person based on his or her characteristic voice with the help of machines. However, this identification is drastically degraded due to adverse conditions. Our aim of this study is to evaluate the performance of the techniques Power- Normalized Cepstral Coefficients (PNCC) and Mel Frequency Cepstral Coefficients (MFCC) on NIST 2002 database, as well as using Wavelet Denoising and Cubic Law as techniques to speech enhancement and nonlinear rectification to improve speaker recognition rates. Results showed that combined Wavelet Denoising and Cubic Law get improved the recognition rates under noisy conditions.

Original languageEnglish
Title of host publicationProceedings - 2022 3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-175
Number of pages6
ISBN (Electronic)9798350331974
DOIs
StatePublished - 2022
Event3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022 - Virtual, Online, China
Duration: 23 Dec 202225 Dec 2022

Publication series

NameProceedings - 2022 3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022

Conference

Conference3rd International Conference on Electronics, Communications and Information Technology, CECIT 2022
Country/TerritoryChina
CityVirtual, Online
Period23/12/2225/12/22

Keywords

  • features
  • noise robustness
  • speaker recognition
  • wavelet denoising cubic law

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