I think for two years we were two months uh away from being done And what was that Morgan What project Uh the uh TORRENT chip Oh Yeah We were two we were Yeah Uh uh we went through it Jim and I went through old emails at one point and and for two years there was this thing saying yeah two months away from being done It was very very believable schedules too I mean we went through and was the true schedules two and years Yeah Oh yeah It was very true So should we just do the same kind of deal where we go around and do uh status report kind of things OK And I guess when Sunil gets here he can do his last or something So we probably should wait for him to come before we do his OK OK good idea Yeah Any objection Do All OK favor Do you want to start Morgan Do you have anything or Uh I don't do anything I No I mean I involved in discussions with with people about what doing but I think since here they can talk about it themselves OK So should I go so that uh Yeah Why don't you go ahead Barry gonna talk about Aurora stuff per se OK OK Um Well this past week just been uh getting down and dirty into writing my my proposal So um Mmm I just finished a section on uh on talking about these intermediate categories that I want to classify um as a as a middle step And um I hope to hope to get this um a full rough draft done by uh Monday so I can give it to Morgan When is your uh meeting Um my meeting Yeah with uh Oh oh you mean the the quals Yeah Uh the quals are happening in July Oh Soon Yeah Yeah So is the idea going to do this paper and then you pass it out to everybody ahead of time and Right right So y you write up a proposal and give it to people ahead of time and you have a short presentation And um and then um then everybody asks you questions Hmm Yeah I remember now Yep So Have um Y was just s gonna ask do you want to say any a little bit about it or Oh Uh Mmm a little bit about Wh what what gonna You said you were talking about the uh particular the features the were looking at or Right Well I was um I think one of the perplexing problems is um for a while I was thinking that I had to come up with a complete set of intermediate features in intermediate categories to to classify right away But what thinking now is I would start with with a reasonable set Something something like um um like uh re regular phonetic features just to just to start off that way And do some phone recognition Um build a system that uh classifies these um these feat uh these intermediate categories using uh techniques Combine them and do phon phoneme recognition Look at then I would look at the errors produced in the phoneme recognition and say OK well I could probably reduce the errors if I included this extra feature or this extra intermediate category That would that would reduce certain confusions over other confusions And then and then reiterate Um build the intermediate classifiers Uh do phoneme recognition Look at the errors And then postulate new or remove um intermediate categories And then do it again So gonna use TIMIT Um for that for that part of the the process yeah I would use TIMIT And um then after after uh um doing TIMIT Right Um um just the ph the phone recognition task Yeah I wanted to take a look at um things that I could model within word So I would mov I would then shift the focus to um something like Schw Switchboard uh where I would be able to um to model um intermediate categories that span across phonemes not just within the phonemes themselves um and then do the same process there um on on a large vocabulary task like Switchboard Uh and for that for that part I would use the SRI recognizer since already set up for for Switchboard And run some some sort of processing with uh my intermediate classifiers Oh So why you were interested in getting your own features into the Yeah SRI files why Yeah I I was asking about that Yeah Yeah Um and I guess it Any any questions Sounds good So you just have a few more weeks huh Um yeah A few more about a month from now a a month and and a week Yeah Yeah So uh you want to go next Dave And do OK sure So um last week I finally got results from the SRI system about this mean subtraction approach And um we we got an improvement uh in word error rate training on the data set and testing on Meeting Recorder digits of um six percent to four point five percent um on the n on the data using PZM F but um the performance worsened um from one point two percent to two point four percent And um wh why would that be um considering that we actually got an improvement in performance using HTK And so uh with some input from uh Andreas I have a theory in two parts Um first of all HTK sorry SR the SRI system is doing channel adaptation and so HTK wasn't Um so this um This mean subtraction approach will do a kind of channel normalization and so that might have given the HTK use of it a boost that wouldn't have been applied in the SRI case And also um the Andreas pointed out the SRI system is using more parameters got acoustic models So those acoustic models could be more sensitive to the artifacts in the audio Um And me and Barry were listening to the audio and sometimes it seems like you get of a bit of an echo of speech in the background And so that seems like it could be difficult for training cuz you could have different phones lined up with a different foreground phone um depending on the timing of the echo So um gonna try training on a larger data set and then eh the system will have seen more examples o of these artifacts and hopefully will be more robust to them So planning to use the Macrophone set of um read speech and um Hmm I had another thought just now which is uh remember we were talking before about we were talking in our meeting about uh this stuff that some of the other stuff that Avendano did where they were um getting rid of