input: Text: Professor A: We're going? OK. Sh - Close your door on <unk>disfmarker<unk> door on the way out? Grad B: OK. Thanks. Professor A: Thanks. Grad B: Oh. Professor A: Yeah. Probably wanna get this other door, too. OK. So. Um. <unk>vocalsound<unk> <unk>vocalsound<unk> What are we talking about today? PhD E: Uh, well, first there are perhaps these uh Meeting Recorder digits that we tested. Professor A: Oh, yeah. That was kind of uh interesting. PhD E: So. Professor A: The <unk>disfmarker<unk> both the uh <unk>disfmarker<unk> <unk>vocalsound<unk> the SRI System and the oth PhD E: Um. Professor A: And for one thing that <unk>disfmarker<unk> that sure shows the <unk>vocalsound<unk> difference between having a lot of uh training data <unk>vocalsound<unk> or not, PhD E: Of data? Yeah. Professor A: uh, the uh <unk>disfmarker<unk> <unk>vocalsound<unk> The best kind of number we have on the English uh <unk>disfmarker<unk> on near microphone only is <unk>disfmarker<unk> is uh three or four percent. PhD E: Mm - hmm. Professor A: And uh it's significantly better than that, using fairly simple front - ends <unk>vocalsound<unk> on <unk>disfmarker<unk> <unk>vocalsound<unk> on the uh <unk>disfmarker<unk> <unk>vocalsound<unk> uh, with the SRI system. PhD E: Mm - hmm. Professor A: So I th I think that the uh <unk>disfmarker<unk> But that's <unk>disfmarker<unk> that's using uh a <unk>disfmarker<unk> a pretty huge amount of data, mostly not
  Question: What did PhD E think about the noise?
  Answer:
output: PhD E speculated that the recording problem probably does not have to do with noise. The difference in result with the tandem and Aurora system was too big. He suggested they could learn more by adding more data from Meeting Recorder digits.