input: Text: PhD F: And we're on. Professor D: OK. Might wanna <unk>vocalsound<unk> close the door so that <unk>disfmarker<unk> Uh, Stephane will <unk>disfmarker<unk> PhD F: I 'll get it. Professor D: Yeah PhD F: Hey Dave? Could you go ahead and turn on, uh, Stephane's <unk>disfmarker<unk> Grad C: Mm - hmm. Professor D: So that's the virtual Stephane over there. PhD F: OK. Professor G: Do you use a PC for recording? Or <unk>disfmarker<unk> PhD F: Uh, yeah, a Linux box. Yeah. It's got, uh, like sixteen channels going into it. Professor G: Uh - huh. Uh - huh. The quality is quite good? Or <unk>disfmarker<unk>? PhD F: Mm - hmm. Yeah, so far, it's been pretty good. Professor G: Mm - hmm. Professor D: Yeah. So, uh, yeah <unk>disfmarker<unk> the suggestion was to have these guys start to <unk>disfmarker<unk> PhD F: OK. Why don't you go ahead, Dave? Grad C: OK. Um, so, yeah, the <unk>disfmarker<unk> this past week I've been main mainly occupied with, um, getting some results, u from the SRI system trained on this short Hub - five training set for the mean subtraction method. And, um, I ran some tests last night. But, um, c the results are suspicious. Um, it's, um, <unk>vocalsound<unk> cuz they're <unk>disfmarker<unk> the baseline results are worse than, um, Andreas <unk>disfmarker<unk> than results Andreas got previously.
  Question: What did the team discuss about high mismatch?
  Answer:
output: The model that PhD B was working on performed better on high mismatch than on the well matched case. The professor thought that this reflected the team's priority, which had always focused more so on the high mismatched case. PhD H was concerned that their model did worse on medium and high mismatch. Adding noise hurt their model.