with a system better than both but clearly the way that operating for this other stuff does involved some latency to to get rid of most of that latency To get down to forty or fifty milliseconds have to throw out most of what doing And and uh I don't think any good reason for it in the application actually I mean speaking to a recognizer on a remote server and uh having a a a quarter second for some processing to clean it up It doesn't seem like that big a deal These aren't large vocabulary things so the decoder shouldn't take a really long time and So And I don't think gonna notice the difference between a quarter of a second of latency and thirty milliseconds of latency No What what does wa was your experience when you were doing this stuff with uh the the the surgical uh uh microscopes and so forth Um how long was it from when somebody uh finished an utterance to when uh something started happening Um we had a silence detector so we would look for the end of an utterance based on the silence detector And I I can't remember now off the top of my head how many frames of silence we had to detect before we would declare it to be the end of an utterance Um but it was uh I would say it was probably around the order of two hundred and fifty milliseconds Yeah and when start doing things Yeah we did the back trace at that point Yeah to get the answer course that didn't take too long at No that point no Yeah was pretty quick Yeah So you you so you had a so you had a a quarter second delay before uh plus some little processing time Right and then the the microscope would start moving or something Right Yeah Right And physical inertia there so probably the the motion itself And was felt to uh the users that it was instantaneous I mean as fast as talking to a person It th I don't think anybody ever complained about the delay Yeah so you would think as long as under half a second or something Yeah Uh not an expert on that but Yeah I don't remember the exact numbers but Yeah was something like that I don't think you can really tell A person I don't think a person can tell the difference between uh you know a quarter of a second and a hundred milliseconds and Yeah not even sure if we can tell the difference between a quarter of a second and half a second Yeah I mean it just it feels so quick I mean basically if you yeah if you said uh um the uh uh the shortest route to the opera and it took half a second to get back to Yeah you I mean it would be f I mean it might even be too abrupt You might have to put in a Yeah s a delay may Yeah feel different than talking to a person because when we talk to each other we tend to step on each utterances So Yeah if asking you a question you may start answering before even done So Right would probably feel different but I don't think it would feel slow Right Well anyway I mean I think we could cut we know what else we could cut down on the neural net time by by uh playing around a little bit going more into the past or something like that We t we talked about that So is the latency from the neural net caused by how far ahead looking And also well the neural net and also this uh uh uh uh KLT Wasn't there Was it in the uh recurrent neural nets where they weren't looking ahead at all They weren't looking ahead much They p they looked ahead a little bit little bit OK Yeah Yeah I mean you could do this with a recurrent net And and then But you also could just um I mean we haven't experimented with this but I imagine you could um uh predict a uh um a label uh from more in the past than in than than in the future I mean d done some stuff with that before I think it it works OK So always had usually we used the symmetric windows but Yeah I but don't think we played a little bit with Yeah with asymmetric guys You can do it So So what what busy with s Uh yeah messing around with this yeah And uh Also we were thinking to to uh apply the eh spectral subtraction from Ericsson and to to change the contextual KLT for LDA Change the what The contextual KLT missing that last word Context KLT KLT KLT Oh KLT KLT sorry Uh to change and use LDA discriminative Yeah But I don't know Uh What is the advantage of that Uh that by the for the moment we have uh something discriminant and nonlinear And the other is linear but not discriminant at all Well a linear transformation that Uh So at least just to understand maybe what the difference was between how much you were getting from just putting the frames together and how much getting from the discriminative what the nonlinearity does for you or doesn't do for you Just to understand it a little better I guess Mmm Well uh yeah Actually what we want to do perhaps to replace to to have something discriminant but linear also And to see if it if it improves ov over over the linear transformation And Hmm if the neural net is better than this or well Yeah well what I meant is to see Ye whether whether Mmm having the neural net really buys you anything Uh I mean it doe did look like it buys you something over just the KLT Yeah But maybe just the discrimination and and maybe yeah maybe the nonlinear discrimination isn't necessary Yeah Maybe be Good good to know But the other part you were saying was the spectral subtraction so you just kind Yeah uh At what stage do you do that Do you doing that um So it would be on the um We was think the mel frequency bands so Yeah Yeah we be f before everything we was thinking to do before after VAD or Oh Yeah um when better Before after VAD or and then so you know that Um that the way that uh one thing that would be no good to find out about from this conference call is that what they were talking about what proposing doing was having a third party um run a good VAD and and determine boundaries Yeah And then given those boundaries Begin to then work have everybody do the recognition The reason for that was that um uh if some one p one group put in the VAD and another didn't