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 started
  Question: Summarize the discussion on feature classification
  Answer:
output: The feature classification had means of 0.3 for voiced and 0.59 for unvoiced on voice-unvoice, though the variance was quite high. The team was using TIMIT and was in the stage of looking for features. The professor thought that the team's intuition was leading them to make statistical characterizations of what was missing from the spectral envelope. They could use things they were potentially losing in the smoothing process.