Evaluation of Hybrid Vector Quantization and Hidden Markov Model Methods in Noisy Environments

Abstract:- In this paper, we presents a comparison between Hidden Markov Model (HMM) and an approach using a hybrid of Vector Quantization (VQ) with HMM methods. The aim of combination scheme used is to improve the standalone HMM performance. A Malay spoken digit database is used for the testing and validation modules. It is shown that, in clean environments, a total success rate (TSR) of 99.97 % is achieved using this hybrid approach. For speaker verification, the true speaker rejection rate is 0.06 % while the impostor acceptance rate is 0.03 % and the equal error rate (EER) is 11.72%. Meanwhile, in noisy environments, TSRs of between 62.57%-76.80 % are achieved for SNRs of 0-30 dBs