Speaker Recognition System Using Combined Vector Quantization and Discrete Hidden Markov model
نویسندگان
چکیده
This paper presents a speaker verification system using a combination of Vector Quantization (VQ) and Hidden Markov Model (HMM) to improve the HMM performance. A Malay spoken digit database which contains 100 speakers is used for the testing and validation modules. It is shown that, by using the proposed combination technique, a total success rate (TSR) of 99.97% is achieved and it is an improvement of 11.24% in performance compared to HMM. For speaker verification, true speaker rejection rate, impostor acceptance rate and equal error rate (EER) are also improved significantly compared to HMM.
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تاریخ انتشار 2012