Wavelet Transform based multistage speaker feature tracking identification system using Linear Prediction Coefficient

In this paper wavelet transform (WT) in its two forms continuous and discrete are used to create text-dependent robust to noise speaker recognition system. The research intends to investigate a high accuracy of identification the speech signal of very difficult nature that is non- stationary. Three methods are used to extract the essential speaker features based on continuous, discrete wavelet transform and linear prediction coefficient (LPC). To have better identification rate three measurement methods are used: percentage rms difference (PRD), correlation coefficient (CC), and statically deformation determination coefficient (SDDC). 95% identification rate is accomplished. The presented system in this paper depends on multi-stage features extracting due to its better accuracy. The system works with excellent capability of features tracking even when the tested signals are very noisy with -32dB SNR. This is accomplished because of multistage features tracking based system using wavelet transform, which is suitable for non-stationary signal.