Um , so , um , I told you I was  I was  I was getting prepared to take this qualifier exam. So basically that 's just , um , trying to propose um , uh , your next your  your following years of  of your PHD work , trying  trying to find a project to  to define and  and to work on. So , I 've been , uh , looking into , um , doing something about r uh , speech recognition using acoustic events. So , um , the idea is you have all these  these different events , for example voicing , nasality , R - coloring , you know burst or noise , uh , frication , that kinda stuff , um , building robust um , primary detectors for these acoustic events , and using the outputs of these robust detectors to do speech recognition. Um , and , um , these  these primary detectors , um , will be , uh , inspired by , you know , multi - band techniques , um , doing things , um , similar to Larry Saul 's work on , uh , graphical models to  to detect these  these , uh , acoustic events. And , um , so I  I been  I been thinking about that and some of the issues that I 've been running into are , um , exactly what  what kind of acoustic events I need , what  um , what acoustic events will provide a  a good enough coverage to  in order to do the later recognition steps. And , also , um , once I decide a set of acoustic events , um , h how do I  how do I get labels ? Training data for  for these acoustic events. And , then later on down the line , I can start playing with the  the models themselves , the  the primary detectors. Um , so , um , I kinda see  like , after  after building the primary detectors I see um , myself taking the outputs and feeding them in , sorta tandem style into  into a um , Gaussian mixtures HMM back - end , um , and doing recognition. Um. So , that 's  that 's just generally what I 've been looking at.
Yeah.
Um ,
By  by the way , uh , the voiced - unvoiced version of that for instance could tie right in to what Carmen was looking at.