input: Text: PhD A: It's not very significant. Professor B: Uh, channel one. Yes. Grad D: Channel three. Professor B: OK. PhD F: Mm - hmm. Grad D: Channel three. PhD A: Ta Grad D: Channel three. Alright. Professor B: OK, did you solve speech recognition last week? Grad E: Almost. Professor B: Alright! Let's do image processing. PhD C: Yes, again. PhD A: Great. PhD C: We did it again, Morgan. Professor B: Alright! Grad E: Doo - doop, doo - doo. PhD A: What's wrong with <unk>disfmarker<unk>? Professor B: OK. It's April fifth. Actually, Hynek should be getting back in town shortly if he isn't already. PhD C: Is he gonna come here? Professor B: Uh. Well, we 'll drag him here. I know where he is. PhD C: So when you said " in town ", you mean <unk>pause<unk> Oregon. Professor B: U u u u uh, I meant, you know, this end of the world, yeah, <unk>vocalsound<unk> is really what I meant, PhD C: Oh. Grad E: Doo, doo - doo. Professor B: uh, cuz he's been in Europe. Grad E: Doo - doo. Professor B: So. PhD C: I have something just fairly brief to report on. Professor B: Mmm. PhD C: Um, I did some <unk>pause<unk> experim uh, uh, just a few more experiments before I had to, <unk>vocalsound<unk> uh, go away for the w well, that week. Professor B: Great! PhD C: Was it last week or whenever? Um, so what I was
  Question: Summarize the discussion about VAD improvements
  Answer:
output: The team thought that it was not fair to get a good VAD since there weren't enough baseline features. An MFCC would have been more appropriate, but optimizing for one meant lower scores on the other. In general, the model was better now due to lower latencies.