How should one select this filter coefficient for a pre-emphasis filter?
If you look at the spectrum of speech sounds, you will notice that less energy is present in the highest frequencies, with an overall decreasing slope. The goal of the pre-emphasis filter is to counter-balance this, and flatten the spectrum. This helps for one or several of the following reasons, depending on your application:


In an analog speech transmission/recording system, applying an emphasis filter on the emitter (recorder) and a de-emphasis filter on the receiver (player) improves the signal to noise ratio for the high frequencies - since they will be recorded/transmitted at a higher amplitude than without pre-emphasis. This is why vinyl records need a special kind of pre-amplifier (RIAA equalization).
In a digital speech processing system implemented with fixed point arithmetics, this avoids truncation errors, by allowing the FFT coefficients carrying the highest frequencies to have a larger magnitude, making better use of the available dynamic range.
In a speech analysis/modeling context (such as linear prediction or sinusoidal modeling), one seeks the model parameters which minimize the distance between the model and the signal. When applying these techniques on speech signals which have not been pre-emphasized, the model will spend a lot of its "parameter space budget" (sinusoids, poles...) trying to overfit the very first harmonics, while ignoring the highest order ones - while these harmonics might actually be relevant for speech intelligibility or phoneme discrimination. Applying pre-emphasis ensures that the model will fit the spectrum more evenly.
The low frequency band is occupied by sounds which are useless/harmful for speech recognition: DC offsets, microphone pops, AC 50Hz/60Hz buzz from ground loops or poor board design, etc. The pre-emphasis filter you mention, which is indeed a simple high-pass filter, helps getting rid of these.


As for the the actual value of $a$, you can try plotting the frequency response of the filter for these different values and/or see where the cutoff frequency is. You could also try to visualize its effect on a small corpus of speech signals - how balanced the spectrum becomes after pre-emphasis. It is largely application-dependent - for example, in some applications, poor microphone quality adds an extra 6dB roll-off of the higher frequencies which need more compensation. In speech recognition, this is the kind of parameter tuned a posteriori to try to get better recognition results - though my experience is that with modern models and recognition techniques it has little influence on performance.