input: Text: Professor C: OK. So uh, he's not here, PhD D: So. Professor C: so you get to <unk>disfmarker<unk> PhD D: Yeah, I will try to explain the thing that I did this <unk>disfmarker<unk> this week <unk>disfmarker<unk> during this week. Professor C: Yeah. PhD D: Well eh you know that I work <unk>disfmarker<unk> I begin to work with a new feature to detect voice - unvoice. PhD E: Mm - hmm. PhD D: What I trying two MLP to <unk>disfmarker<unk> to the <unk>disfmarker<unk> with this new feature and the fifteen feature uh from the eh bus base system PhD E: The <unk>disfmarker<unk> the mel cepstrum? PhD D: No, satly the mes the Mel Cepstrum, the new base system <unk>disfmarker<unk> the new base system. PhD E: Oh the <unk>disfmarker<unk> PhD D: Yeah, we <unk>disfmarker<unk> PhD E: OK, the Aurora system. PhD D: yeah the Aurora system with the new filter, VAD or something like that. PhD E: OK. PhD D: And I'm trying two MLP, one one that only have t three output, voice, unvoice, and silence, Professor C: Mm - hmm. PhD D: and other one that have fifty - six output. The probabilities of the allophone. And I tried to do some experiment of recognition with that and only have result with <unk>disfmarker<unk> with the MLP with the three output. And I put together the fifteen features and the three MLP output. And, well, the result are li a little bit better, but more or less similar. Professor C: Uh, I <unk>disfmarker<unk> I'm <unk>disfmarker<unk> I '
  Question: What did Grad A explain about his vectors for his support vector machine?
  Answer:
output: Grad A explained that his vector contained binary values for whether a phonological feature exists or not. The goal was to come up with a mapping from a feature set to the existence of a particular phonological feature. He was not doing the mapping yet. The goal was simply detecting features at the time.