Modified PSOLA-Genetic Algorithm Based Approach for Voice Re-Construction

The process by which we try to reconstruct or regenerate a voice sample from a source sample or try to modify a source voice to a desirable voice, is called synthetic voice generation or artificial voice or voice conversion. The basic and conventional remedies to overcome this issue are based on training and applying conversion functions which generally require a suitable amount of pre-stored training data from both the source and the target speaker. The paper deals with a very crucial issue of achieving the required prosody, timber and some other unique voice templates by considerably reducing the dependence on the sample training dataset of voice. We needed to find out a way by which we can have templates of the "to be achieved voice" which are nearly same parametrically. This is achieved by assigning a marker to the target voice sample for training. A proper estimation of the transformation function can be made possible only by the above mentioned data. We can get the process done by pre-existing methods. In a nutshell, what we proposed is a system by which even in the scarce availability of training dataset, we can reach to a considerable amount of closeness of the target voice. Even though there is a disadvantage that to have higher precision and closer resemblance, we need to have clear idea of the system of spelling that a language uses.