input: Text: PhD A: Alright. We're on. Professor B: Test, um. Test, test, test. Guess that's me. Yeah. OK. Grad D: Ooh, Thursday. Professor B: So. There's two sheets of paper in front of us. PhD A: What are these? PhD E: Yeah. So. Professor B: This is the arm wrestling? PhD C: Uh. Yeah, we formed a coalition actually. PhD E: Yeah. Almost. PhD C: We already made it into one. Professor B: Oh, good. PhD C: Yeah. Professor B: Excellent. PhD E: Yeah. Professor B: That's the best thing. PhD E: Mm - hmm. Professor B: So, tell me about it. PhD E: So it's <unk>disfmarker<unk> well, it's <unk>pause<unk> spectral subtraction or Wiener filtering, um, depending on if we put <unk>disfmarker<unk> if we square the transfer function or not. Professor B: Right. PhD E: And then with over - estimation of the noise, depending on the, uh <unk>disfmarker<unk> the SNR, with smoothing along time, um, smoothing along frequency. Professor B: Mm - hmm. PhD E: It's very simple, smoothing things. Professor B: Mm - hmm. PhD E: And, um, <unk>vocalsound<unk> the best result is <unk>vocalsound<unk> when we apply this procedure on FFT bins, uh, with a Wiener filter. Professor B: Mm - hmm. PhD E: And there is no noise addition after <unk>disfmarker<unk> after that. Professor B: OK. PhD E: So it's good because <unk>vocalsound<unk> <unk>vocalsound<unk> it's difficult when we have to add noise to
  Question: What did the professor say about the final model?
  Answer:
output: The professor was happy to hear that the team had already started putting together a final software. The professor also noted that Finnish and Spanish had the smallest overall number compared to Aurora.