Imputations of Hostile Conditions in Automatic Speaker Recognition Performance
J.V. Thomas Abraham1, A. Nayeemulla Khan2
1J.V. Thomas Abraham, Department of Computing Science and Engineering, VIT University Chennai Campus, Chennai (Tamil Nadu), India.
2A. Nayeemulla Khan, Department of Computing Science and Engineering, VIT University Chennai Campus, Chennai (Tamil Nadu), India.
Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 2908-2911 | Volume-8 Issue-7, May 2019 | Retrieval Number: G5811058719/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Automatic Speaker Recognition (ASR) is a process in which the person is identified or the claim made by the person is verified. In the last three or four decades lot of researches have been done in this field and it has evolved a lot over these period. But in a real world scenario, performances of these speaker recognition systems have failed in hostile conditions. Building a robust speaker recognition system is a challenging task and should address all types of distortions. In this paper, the performance of a speaker recognition system in hostile conditions is analysed and presented. Especially how the environmental noise imputes the speaker recognition system is studied using the MSR Identity toolbox. Test was conducted with clean speech signals and noisy speech signals at various SNRs. The outcome of the test clearly indicates that the accuracy of the ASRs is degraded in hostile conditions. The results may be used to come up with more robust ASR systems.
Keyword: Speaker Identification Verification, MFCC, GMM-UBM, I-Vectors, Noise Speech, Robust Speaker Recognition
Scope of the Article: High Performance Computing