input: Text: PhD A: OK, we're going. PhD D: Damn. Professor C: And uh Hans - uh, Hans - Guenter will be here, um, I think by next <unk>disfmarker<unk> next Tuesday or so. PhD B: Oh, OK. PhD D: Mm - hmm. Professor C: So he's <unk>disfmarker<unk> he's going to be here for about three weeks, PhD B: Oh! That's nice. PhD A: Just for a visit? Professor C: and, uh <unk>disfmarker<unk> Uh, we 'll see. PhD A: Huh. Professor C: We might <unk>disfmarker<unk> might end up with some longer collaboration or something. PhD A: Cool. Professor C: So he's gonna look in on everything we're doing PhD D: Mm - hmm. Professor C: and give us his <unk>disfmarker<unk> his thoughts. And so it 'll be another <unk>disfmarker<unk> another good person looking at things. PhD B: Oh. Hmm. Grad E: Th - that's his spectral subtraction group? Professor C: Yeah, Grad E: Is that right? Professor C: yeah. Grad E: Oh, OK. So I guess I should probably talk to him a bit too? Professor C: Oh, yeah. Yeah. Yeah. No, he 'll be around for three weeks. He's, uh, um, very, very, easygoing, easy to talk to, and, uh, very interested in everything. PhD A: Really nice guy. Professor C: Yeah, yeah. PhD B: Yeah, we met him in Amsterdam. Professor C: Yeah, yeah, he's been here before. PhD B: Oh, OK. Professor C: I mean, he '
  Question: Summarize the meeting
  Answer:
output: The meeting began with a short introduction to Hans Guenter, who was arriving soon and would be visiting for a few weeks. The team then discussed integrating mean log magnitude spectral subtraction into the SmartKom system. It seemed, however, that lengthening the time for this task was not very effective. The team also learned more about Wiener filtering which worked well when mixed with other approaches. It was almost as effective as the best systems. The team then delved into spectral subtraction and discussed the various ways in which it could be refined. The meeting ended with comments on the additional latency that was caused by this method.