In this section, we will be doing runs using our big static decoding graph (yay!) on a small 10-utterance WSJ test set. Before doing this part, you should recompile your code with optimization on so things will run faster. Enter the following commands:
smk -all -O2 -DNDEBUG Lab4_DP.o smk DcdLab4 |
First, let us look into the impact of various modeling decisions. Our baseline system contains 3k 8-component GMM's running on MFCC's with delta's and delta-delta's. Let's see what happens if we reduce the number of Gaussians per mixture; run the following scripts:
lab4p5.gmm8.sh lab4p5.gmm4.sh lab4p5.gmm2.sh lab4p5.gmm1.sh |
Now, let's see how much delta's and delta-delta's help. Run the following scripts:
lab4p5.mfccdd.sh lab4p5.mfccd.sh lab4p5.mfcc.sh |
Finally, let's see how pruning affects performance. Run the following scripts:
lab4p5.10.none.sh lab4p5.5.none.sh lab4p5.2.none.sh lab4p5.none.10k.sh lab4p5.none.5k.sh lab4p5.none.2k.sh lab4p5.10.10k.sh lab4p5.5.5k.sh |