Finished my first draft of proposal. I read some blogs about speaker verification tech and found out that I was wrong on some aspects (actually I was confused). Those blogs help me understand more and deeper about speaker verification. So I revised my framework and flowcharts: take voice input -> feature extraction -> modeling -> database. The modeling part is the most difficult part in speaker verification. The most popular models are: Hidden Markov Model, Gaussian Mixture Model, Vector Quantization, etc. I am not sure which one I will use for sure. It all depends on my dataset and customer need. I need to experiment several models to know which one I want the best. But I chose GMM temporarily on my proposal.