input: Text: Professor A: OK. Grad B: OK we're on and we seem to be working. PhD C: Yes. Professor A: OK. Grad B: We didn't crash <unk>disfmarker<unk> we're not crashing anymore PhD C: One, two, three, four, f Grad B: and it really bothers me. Professor A: Yeah? PhD C: No crashing. PhD G: I do. I crashed when I started this morning. Grad B: You crashed <unk>disfmarker<unk> crashed this morning? I did not crash this morning. PhD C: Yeah? Professor A: Oh! Well maybe it's just, you know, how many t u u u u how many times you crash in a day. PhD G: Really? Yeah. Maybe, yeah. Professor A: First time <unk>disfmarker<unk> first time in the day, you know. PhD G: Or maybe it's once you've <unk>pause<unk> done enough meetings <unk>comment<unk> it won't crash on you anymore. PhD E: Yeah. PhD C: No? Postdoc F: Yeah. PhD G: It's a matter of experience. PhD E: Yeah. Professor A: Yeah. Postdoc F: Self - learning, yeah. Professor A: That's <unk>disfmarker<unk> that's great. PhD G: Yeah. Professor A: Uh. PhD C: Yeah. Professor A: Do we have an agenda? Liz <unk>disfmarker<unk> Liz and Andreas can't sh can't <unk>disfmarker<unk> uh, can't come. Grad B: I do. Professor A: So, they won't be here. Grad B: I have agenda and it's all me. PhD G: Did <unk>disfmarker<unk> Grad B: Cuz no one sent me anything else. PhD G: Did they send, uh, the messages to you about the meeting today
  Question: What problems were encountered with transcriptions?
  Answer:
output: Transcribers are likely to overlook backchannels in densely populated sections of speaker overlap. Speaker mn014 reported that this is also problematic for the automatic detection of speech and non-speech, as backchannels that are very short and not loud enough will inevitably be overlooked. Speaker mn005 reported problems distinguishing between possible harmonics and other frequency peaks, and creating an algorithm for obtaining the instantaneous frequency. The encoding of all audible breaths is too time-consuming.