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
  Question: Summarize the discussion on support vector machines to map MFCC to phonological features
  Answer:
output: Support vector machines were better at dealing with a lower amount of data, so they could do a reasonable job learning patterns in MFCC without too much work. They worked by finding an optimal separating plane. This was more efficient as the model picked only critical points as opposed to doing more computationally expensive k-means clustering.