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 <unk>vocal
  Question: Summarize the meeting
  Answer:
output: The team discussed SRI experiments, multi-band approaches, latency, VAD, and noise addition. The mean subtraction approach for SRI that relied on a twelve second interval resulted in a zero point three percent improvement over the six second model, not necessarily significant. The team thought that training the model on different speeds of speech would be helpful to add to this progress. Grad A introduced the team to her formal proposal for the qualifier exam. The work focused on intermediate classifiers and multi-band graphical models for speech recognition. The team then discussed noise removal as well The model PhD B was working on did not do well in non-stationary noise environments. In general, however, the model had improved. The VAD was labeling speech as silence segments, which could be attributed to alignment issues. The team thought that noise addition techniques might help the model.