Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/395192
Title: Enhancing speaker verification in noisy environments using recursive least-squares (RLS) adaptive filter
Authors: Mohd Zaizu Ilyas
Salina Abdul Samad
Aini Hussain
Khairul Anuar Ishak
Conference Name: International Symposium on Information Technology
Keywords: Recursive least-squares (RLS)
Total success rate (TSR)
Hidden Markov Model (HMM)
Noisy environments
Conference Date: 26/08/2008
Conference Location: Kuala Lumpur Convention Centre
Abstract: In this paper, we present a speaker verification system based on the Hidden Markov Model (HMM) technique and Recursive Least Squares (RLS) adaptive filtering. The aim of using RLS adaptive filtering is to improve the HMM performance in noisy environments. A Malay spoken digit database is used for the testing and validation modules. It is shown that, in a clean environment a total success rate (TSR) of 89.97% is achieved using HMM. For speaker verification, the true speaker rejection rate is 25.3% while the impostor acceptance rate is 9.99% and the equal error rate (EER) is 16.66%. In noisy environments without RLS adaptive filtering TSRs of between 43.07%-51.26% are achieved for SNRs of 0-30 dBs. Meanwhile, after RLS filtering, TSRs of between 50.95%-56.75% are achieved for SNRs 0-30 dB.
Pages: 6
Call Number: T58.5.C634 2008 kat sem j.4
Publisher: Institute of Electrical and Electronics Engineers (IEEE),Piscataway, US
Appears in Collections:Seminar Papers/ Proceedings / Kertas Kerja Seminar/ Prosiding

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