input: Text: PhD A: OK, we're on. Professor C: OK, what are we talking about today? PhD B: I don't know. Do you have news from the conference talk? Uh, that was programmed for yesterday <unk>disfmarker<unk> I guess. Professor C: Uh <unk>disfmarker<unk> PhD D: Yesterday Professor C: Uh <unk>disfmarker<unk> PhD D: Yesterday morning on video conference. Professor C: Uh, PhD B: Well Professor C: oh, I'm sorry. Grad E: Oh. Conference call. Professor C: I know <unk>disfmarker<unk> now I know what you're talking about. No, nobody's told me anything. PhD B: Alright. PhD A: Oh, this was the, uh, talk where they were supposed to try to decide <unk>disfmarker<unk> PhD B: To <unk>disfmarker<unk> to decide what to do, PhD A: Ah, right. PhD B: yeah. PhD D: Yeah. Professor C: Yeah. No, that would have been a good thing to find out before this meeting, that's. No, I have no <unk>disfmarker<unk> I have no idea. Um, Uh, so I mean, let's <unk>disfmarker<unk> let's assume for right now that we're just kind of plugging on ahead, PhD B: Yeah. Professor C: because even if they tell us that, uh, the rules are different, uh, we're still interested in doing what we're doing. So what are you doing? PhD B: Mm - hmm. Uh, well, we've <unk>disfmarker<unk> a little bit worked on trying to see, uh, what were the bugs and the problem with the latencies. PhD D: To improve <unk>disfmarker<unk> PhD B: So
  Question: What did Grade E think about sonorance?
  Answer:
output: Grad E started the discussion on sonorance and informed the team of the model by Saul, Allen, and Rahim. The model detects phonetic features using independent tests. He explained that several detectors trained on different features were more robust than a full-band of Gaussian mixtures.