Normalized Least Mean Square adaptive noise cancellation filtering for speaker verification in noisy environments

In this paper, we present a speaker verification system based on the hidden Markov models (HMMs) and normalized least mean square (NLMS) adaptive filtering. The aim of using NLMS adaptive filtering is to improve the HMMs 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 HMMs. 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 NLMS adaptive filtering TSRs of between 43.07%-51.26% are achieved for SNRs of 0-30 dBs. Meanwhile, after NLMS filtering, TSRs of between 55.18%-55.30% are achieved for SNRs 0-30 dB.