input: Text: Professor A: We're going? OK. Sh - Close your door on <unk>disfmarker<unk> door on the way out? Grad B: OK. Thanks. Professor A: Thanks. Grad B: Oh. Professor A: Yeah. Probably wanna get this other door, too. OK. So. Um. <unk>vocalsound<unk> <unk>vocalsound<unk> What are we talking about today? PhD E: Uh, well, first there are perhaps these uh Meeting Recorder digits that we tested. Professor A: Oh, yeah. That was kind of uh interesting. PhD E: So. Professor A: The <unk>disfmarker<unk> both the uh <unk>disfmarker<unk> <unk>vocalsound<unk> the SRI System and the oth PhD E: Um. Professor A: And for one thing that <unk>disfmarker<unk> that sure shows the <unk>vocalsound<unk> difference between having a lot of uh training data <unk>vocalsound<unk> or not, PhD E: Of data? Yeah. Professor A: uh, the uh <unk>disfmarker<unk> <unk>vocalsound<unk> The best kind of number we have on the English uh <unk>disfmarker<unk> on near microphone only is <unk>disfmarker<unk> is uh three or four percent. PhD E: Mm - hmm. Professor A: And uh it's significantly better than that, using fairly simple front - ends <unk>vocalsound<unk> on <unk>disfmarker<unk> <unk>vocalsound<unk> on the uh <unk>disfmarker<unk> <unk>vocalsound<unk> uh, with the SRI system. PhD E: Mm - hmm. Professor A: So I th I think that the uh <unk>disfmarker<unk> But that's <unk>disfmarker<unk> that's using uh a <unk>disfmarker<unk> a pretty huge amount of data,
  Question: What did the professor think about the back-end?
  Answer:
output: The professor thought that they were constrained by the shortage of data in the backend. Bigger companies had massive amounts of data to train their models. He recalled that they had been playing with the states in the back-end, but the results had not been too fruitful.