Research On Robust Text-Independent Speaker Identification System In Noise

The goal of speaker identification systems is to extract, characterize and identify the information in the speech signal conveying speaker identity. So they have wide applied prospect in the identity authentication domain. The paper involves two parts of closed-set text-independent speaker identification systems in noise.Firstly, the paper introduces shortcomings of the parameters, such as LPCC, MFCC etc. So sub-band energy cepstral feature parameters based on multi-rate sub-band processing and Teager energy operator are described. While the paper discusses the use of rayleigh cepstral liftering to weigh sub-band cepstral coefficients so as to emphasize speaker personality information.Secondly, the paper proposes probabilistic neural networks (PNN) methods of speaker identification, and thoroughly researches the models, training algorithms, real-time fabric, noise robustness and network structure of PNN.(1)The heteroscedastic PNN model with training algorithms that is a mixture of Gaussian basis functions having different variances is considered. (2)An efficient training algorithm for PNN using the minimum classification error criterion is presented.(3)A real-time speaker identification system that introduces acoustic classification information into a heteroscedastic PNN model is proposed.(4)A novel noise-adaptive updating and training approach is proposed for PNN classification.(5)A novel PNN model with training algorithms is proposed for class conditional density estimation.Lastly, speaker identification experiments results using Matlab or VC++ program tool indicate that the proposed parameters, models and algorithms improve identification accuracy in noise...