Professor B: What happens there is it 's around the same and so you could reduce the dimension as you were saying before a bit perhaps .
PhD C: Yeah , it 's it 's significantly worse well but Mm - hmm .
Professor B: It 's significantly worse it 's it 's uh it 's it 's mostly worse .
PhD C: Exc - except for the HM
PhD D: For many a mismatch it 's worse .
PhD C: but
Professor B: Yeah . But it is little . I mean not not by a huge amount , I don't know . What are what are the sizes of any of these sets , I I 'm I 'm sure you told me before , but I 've forgotten . So you know how many words are in uh one of these test sets ?
PhD C: Uh
PhD D: I don't remember .
Professor B: About ?
PhD C: Um it 's it depends well the well matched is generally larger than the other sets and I think it 's around two thousand or three thousand words perhaps , at least .
PhD D: Ye But words well word I don't know .
PhD C: Hmm ? The words , yeah . S sentences .
PhD D: Sentences .
PhD C: Some sets have five hundred sentences , so .
PhD D: Yeah .
Professor B: So the so the sets so the test sets are between five hundred and two thousand sentences , let 's say
PhD C: Mmm .
Professor B: and each sentence on the average has four or five digits or is it most of them longer or
PhD C: Yeah .
PhD D: Yeah for the Italian even seven digits y more or less
PhD C: It it d Seven digits .
PhD D: but sometime the sentence have only one digit and sometime uh like uh the number of uh credit cards , something like that .
Professor B: Mm - hmm . Right , so between one and sixteen . See the I mean the reason I 'm asking is is is we have all these small differences and I don't know how seriously to take them , right ?
PhD C: Mm - hmm ?
Professor B: So uh i if if you had uh just you know to give an example , if you had uh um if you had a thousand words then uh a a tenth of a percent would just be one word ,
PhD C: Yeah .
Professor B: right ? So so it wouldn't mean anything .
PhD D: Yeah .
Professor B: Oh
PhD C: Yeah .
Professor B: um so um yeah it be kind of I 'd kind of like to know what the sizes of these test sets were actually .
PhD C: Yeah .
PhD D: The size that we have ?
PhD C: We could we could run run some kind of significance tests
Professor B: Yeah since these well also just to know the numbers ,
PhD C: or
PhD D: Yeah .
Professor B: right . So these these are word error rates
PhD C: Yeah .
Professor B: so this is on how many words .
PhD C: Yep .
PhD D: Yeah we have the result that the output of the HTK
Professor B: Yeah .
PhD D: The number of of sentences , no it 's the number isn't .
PhD C: Yeah sure sure . Yeah sure .
Professor B: Yeah so anyway if you could just mail out what those numbers are and then then that that be great .
PhD C: Yeah .
PhD D: Yeah .
Professor B: Um what else is there here ? Um see the second second from the bottom it says SIL , but this is some different kind of silence or thing or what was that ?
PhD C: Uh
PhD D: It the the output silence of the MLP .
PhD C: Oh yeah I see .
PhD D: It 's only one small experiment to know what happened . To apply also to in include also the the silence of the MLP we have the fifty - six form and the silence to pick up the silence and we include those .
Professor B: Yes . Uh - huh , uh - huh . The silence plus the KLT output ? Oh so you 're only using the silence .
PhD C: Yeah .
PhD D: Yeah , because when we apply the KLT
PhD C: No they 're I think there is this silence in addition to the um KLT outputs
Professor B: No .
PhD D: in addition , yes .
PhD C: it is because we we we just keep uh we don't keep all the dimensions after the KLT
PhD D: In addition t
PhD C: and yeah .
PhD D: and we not s we are not sure if we pick we have the silence .
PhD C: So we try to add the silence also in addition to the these twenty - eight dimensions .
Professor B: I see . OK . And what and what 's OGI forty - five ? The bottom one there ?
PhD C: Uh it 's o it 's OGI two , it 's so the th it 's the features from the first line
PhD D: It 's in fact OGI two .
Professor B: S
PhD C: and yeah .
Professor B: Right , but I mean what 's the what does the last row mean ?
PhD C: So it 's uh basically this but without the KLT on the from the left path .
Professor B: I thought that was the one I thought that was the second row . So what 's the difference between the second
PhD C: Uh the second line you don't have this combo stuff so you just
Professor B: Oh .
PhD C: uh
Professor B: So this is like the second line but with with the combo stuff .
PhD C: Yeah . Yeah .
PhD D: And with the all the output of the combo .
Professor B: OK . Yeah .
PhD C: Yeah .
PhD D: Uh
Professor B: OK , so alright so it looks to me I guess the same given that we have to take the filt ones out of the the running because of this delay problem so it looks to me like the ones you said I agree are are the ones to look at
PhD C: Mm - hmm .
Professor B: but I just would add the the the second row one
PhD C: Yeah .
Professor B: and then um if we can um
PhD C: Mmm .
Professor B: oh yeah also when when they 're using this weighting scheme of forty , thirty - five , twenty - five is that on the percentages or on the raw errors ? I guess it 's probably on the percentages right ?
PhD C: Uh I guess , yeah .
Professor B: Yeah OK .
PhD C: I guess , yeah . Mmm .
Professor B: Alright .
PhD C: It 's not clear here .
Professor B: OK . Maybe maybe they 'll argue about it . Um OK so if we can know what how many words are in each and then um Dave uh Dave promised to get us something tomorrow which will be there as far as they 've gotten Friday
PhD C: Mm - hmm .
Professor B: and then we 'll operate with that
PhD C: Yeah .
Professor B: and uh how long did it I guess if we 're not doing all these things if we 're only doing um um I guess since this is development data it 's legitimate to do more than one , right ? I mean ordinarily if in final test data you don't want to do several and and take the best
PhD C: Yeah . Mmm .
Professor B: that 's that 's that 's not proper but if this is development data we could still look at a couple .