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. And
  Question: Summarize the discussion on noise addition
  Answer:
output: Too much noise was causing the model to perform worse, so the noise addition was constant and not too high. The overall improvement was good. The smoothing for the average for noise addition was only done on low gains. The model performed better on high mismatch.