Enhancing speaker verification in noisy environments using Recursive Least-Squares (RLS) adaptive filter

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.