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Robustness of speaker recognition from noisy speech samples and mismatched languages

Al-Noori, AHY; Li, FF; Duncan, PJ


AHY Al-Noori


PJ Duncan


Speaker recognition systems can typically attain high performance in ideal conditions. However, significant degradations in accuracy are found in channel-mismatched scenarios. Non-stationary environmental noises and their variations are listed at the top of speaker recognition challenges. Gammtone frequency cepstral coefficient method (GFCC) has been developed to improve the robustness of speaker recognition. This paper presents systematic comparisons between performance of GFCC and conventional MFCC based speaker verification systems with a purposely collected noisy speech data set. Furthermore, the current work extends the experiments to include investigations into language independency features in recognition phases. The results show that GFCC has better verification performance in noisy environments than MFCC. However, GFCC show more sensitivity to language mismatch between enrolment and recognition phase.


Al-Noori, A., Li, F., & Duncan, P. (2016, June). Robustness of speaker recognition from noisy speech samples and mismatched languages. Presented at 140th Convention-AES, Paris

Presentation Conference Type Other
Conference Name 140th Convention-AES
Conference Location Paris
Start Date Jun 4, 2016
End Date Jun 7, 2016
Acceptance Date Jun 3, 2016
Online Publication Date May 26, 2016
Publication Date May 26, 2016
Deposit Date Apr 11, 2016
Publisher URL
Related Public URLs
Additional Information Event Type : Conference
Funders : The Ministry of Higher education and Scientific research - Iraq