Skills you will need

Experimental design

You need to think carefully about each experiment, and what it is designed to discover. Do not simply run random, ill-thought-out experiments and then try to make sense of the results. Instead, form a hypothesis, and express it as a falsifiable statement, like this:

When training models only on speaker A, the word error rate will be lower for speaker A’s test data, than for test data from speaker B.

and then design an experiment to test whether that is true.  In the report you should also take care to explain why you formulated the hypothesis the way you did (i.e., why did you think it might be true?  Do you have evidence from the literature? Observations of other speakers?)

The key to a good experimental design here is to control any factors that you are not interested in, and only investigate a single factor (e.g., speakers’ gender) per experiment.

Shell scripting

A few fairly simple shell scripting techniques will help you enormously here. You can completely automate your experiments. This not only makes them more efficient for you, it also makes it easier to reproduce your results.

There are several resources in the “Intermission” lab tab if you need some help getting started with the shell, particular this intro to shell scripting. You may also find this bash primer by Jason Fong helpful!