Determine the time length of audio training samples? Currently I am trying to use MFCC feature for audio training in order to do human voice classification (can later distinguish the person who speaks).

I have problems to determine the proper time length of audio training samples? To be clear, say I have computed 12d-MFCC features over 20ms window (10ms overlapping), if the length of the audio training samples is 1s, this will result in a 12 x 100 feature vector (as there are 100 frames during the 1s time given 10ms shifting time step). In practice, what's the proper time length of audio training samples? Of course, long time length will results in long MFCC feature vector.
It's a very bad idea to stack together consecutive frames in a single vector. By doing so, you are training a classifier to recognize the exact same sound as the one in your training database, pronounced at the exact same speed.

For your application (which appears to be speaker identification), you should consider each individual frame as a training instance (aka "bag-of-frames" models). That is to say, each training sample will give many 12-dimension feature vectors.