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
  Question: Summarize the discussion on the mean log magnitude spectral subtraction
  Answer:
output: The log magnitude spectral subtraction used twelve seconds from the past and future to calculate the mean. The short window did not have a big effect on the model performance, which was much desired. The silence, for the most part, had been cut out to make the model more effective. The professor added that the system was similar to caching utterances.