CAT approach

CAT

Intoduction

After reading the basic paper about Cluster Adaptive Training ( CAT ) , I got a good Idea about the I-Vector approach and the statistics behind it.

CAT did provide a method to reduce substantially the amount of Parameters for training, but increased the performance and accuracy of the system.

CAT does rely on GMM-HMM adaptation , and clusters similar speakers together, but ties the variances and prior probabilities together hence only the means vary between clusters.

component priors and variances are tied over all the speaker clusters. 

Definition

The challenge is to calculate the mean of the speakers , given a Gaussian component.

This results in the following Model:

To estimate these two unknown variables, EM is used. The Estimation formula is defined as:

Beginning of Thesis

So it's on, shit is on fire, I began my Bachelor's Thesis. I started this blog just in case I need to share some results or just publish any kind of problems that did occur doing its conduction.

Topic is "short-time speaker verification with deep learning", which is in the Field of Human-Machine Interaction or to be more precise, Speech Recognition.

Mainly it evolves around to introduce several algorithms into Cambridge's HTK framework.