- The Mel-Frequency Cepstral Coefficient (MFCC) of the FFT (Fast Fourier
Transform) of a waveform is a good timbral identifier. William Brent's TimbreID objects are good instantaneous timbre identifiers using this principle, but to build up a sophisticated model of a human voice (robust enough for speaker ID) you need to work out how to build a database. For an instantaneous MFCC identifier using an internal database, check out Michael Casey's "soundspotter" PD external.
Aside from the different analysis objects like [mfcc~], there is an object in the timbreID library that makes it easy to build a training database and make comparisons on the fly. But like Ed and others are saying - the problem is how to interpret the stored data. I never dove into the voice recognition problem, but my understanding is also that the magic is in the transitions. timbreID will help you get all the data you need if you can go the Markov model route. On the other hand, if I were going to take a stab at a simplified system based on isolated sounds, in general I'd guess that features of pure vowels would be more helpful in distinguishing between different speakers than features of "sss" sounds or consonants.