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
  Question: Summarize the discussion on SRI experiments and mean subtraction
  Answer:
output: The SRI system was doing worse on the hub-five training set for mean subtraction. The model performed better on six seconds of training data rather than twelve seconds, but the improvement was only point three percent. The professor thought small differences were important for research but not as important in industry. One suggestion for further work was changing speech rate using the algorithm, though it came at the risk of degrading the data.