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Content Provider | IEEE Xplore Digital Library |
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Author | Gabrea, M. |
Copyright Year | 2011 |
Description | Author affiliation: École de Technologie Supérieure - Electrical Engineering Department, 1100, Notre-Dame West, Montreal, Quebec, Canada H3C 1K3 (Gabrea, M.) |
Abstract | In this paper, we study the performance limits of a standard GMM speaker identification (SI) system in “adverse conditions” context using several real noises. Adaptive noise cancellation represents one such potentially effective technique and refers to a class of adaptive enhancement algorithms based on the availability of a primary input source and a secondary reference source. It will be shown that with the use of an adaptive noise cancelling called the Double Generalized Least Mean Squares (DGLMS), the SI performance can approach the optimal performance system. Experiments are done on Spidre corpus corrupted by different type of noises. The performance of the SI is improved by more than 35%. |
Starting Page | 000569 |
Ending Page | 000572 |
File Size | 175154 |
Page Count | 4 |
File Format | |
ISBN | 9781424497881 |
ISSN | 08407789 |
e-ISBN | 9781424497898 |
e-ISBN | 9781424497874 |
DOI | 10.1109/CCECE.2011.6030515 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-05-08 |
Publisher Place | Canada |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Speech Noise cancellation Noise measurement Hidden Markov models Silicon Finite impulse response filter instrumental variables Speaker identification adaptive filtering noise canceller |
Content Type | Text |
Resource Type | Article |
Subject | Electrical and Electronic Engineering Hardware and Architecture |
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