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: What did PhD A think about VAD?
  Answer:
output: PhD A thought that a good VAD could not be made without baseline features. On-line normalization was not helping the model when spectral subtraction was already in play. The short filters were performing as well as the long ones, and the model was doing well on the well-matched case. Despite mixed results, the shorter latency was a positive sign.