I'm working on a project to transcribe lecture videos. We are currently just using humans to do the transcriptions as we believe it is easier to transcribe than editing ASR, especially for technical subjects (not the point of my question, though I'd love any input on this). From our experiences we've found that after about 10 minutes of transcribing we get anxious or lose focus. Thus we have been splitting videos into ~5-7 minute chunks based on logical breaks in the lecture content. However, we've found that the start of a lecture (at least for the class we are piloting) often has more talking than later on, which often has time where the students are talking among themselves about a question. I was thinking that we could do signal processing to determine the rough amount of speaking throughout the video. The idea is to break the video into segments based on the amount of typing they require, as opposed to splitting based on time.
I've done a little research into this, but everything seems to be a bit overkill for what I'm trying to do. The videos for this course, though we'd like to generalize, contain basically just the lecturer with some occasional feedback and distant student voices. So can I just simply look at the waveform and roughly use the spots containing audio over some threshold to determine when the lecturer is speaking? Or is an ML approach really necessary to quantify the lecturer's speaking?
Hope that made sense, I can clarify anything if necessary.
Appreciate the help as I have no experience with signal processing.