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 PhD E think about improving the model?
  Answer:
output: PhD E thought that changing a few things could result in an improvement, but they had to be careful with the neural net. PhD also played around with noise estimation to improve the model but did not play around with it much.