NDLI: Adaptive filtering for noisy and mismatched conditions in speaker recognition

Content Provider	IEEE Xplore Digital Library
Author	Malik, S. Saqib, N.A.
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, National University of Sciences and Technology SEECS, Islamabad, Pakistan (Malik, S.; Saqib, N.A.)
Abstract	This paper presents comparison of different adaptive filter algorithms i.e. Wiener, Least Mean Square LMS, Normalized Least Mean Square NLMS, Block Least Mean Square, BLMS, as system identifiers of telephonic channel; specially when there is a mismatch in train and test conditions of speaker recognition system. We need to know the response of this telephonic channel in order to minimize the mismatch between training and testing data. Once this response is known we can get filter weights either to model telephonic channel and pass the clean speech through it or make and inverse system out of the identified coefficients in order to enhance the corrupted signal. Results are obtained on white Gaussian noise added signal and signal passed through telephonic channel. It has been seen that LMS and NLMS perform better over white Gaussian noise added signals whereas in case of mismatched train/test conditions, the better estimate of unknown telephonic channel response was given by LMS algorithm with small step size of 0.003 and then at 0.5, 0.6 step size. Effect of varying step size has been observed not only for LMS but it's variants as well. Overall results show significant improvement from 20% to 80-66% in speaker recognition accuracy.
Starting Page	162
Ending Page	167
File Size	831094
Page Count	6
File Format	PDF
ISBN	9781457702433
e-ISBN	9781457702426
DOI	10.1109/ICSIPA.2011.6144152
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-16
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	NLMS LMS Mismatched conditons Adaptive filters Hidden Markov models Speech recognition Adaptive filtering Speech Speaker recognition Noise measurement Least squares approximation BLMS
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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