Determining the 'amount' of speaking in a video - audio

Question

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 containing roughly the same amount of lecturing, as opposed to segments that are the same length.
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.

Answer 1

Although there are machine learning mehtods that are very good at discriminating voice from other sounds, you don't seem to require that sort of accuracy for your application. A simple level-based method similar to the one you proposed should be good enough to get you an estimate of speaking time.
Level-Based Sound Detection
Goal
Given an audio sample, discriminate the portions with a high amount of sounds from the portions that consist of background noise. This can then be easily used to estimate the amount of speech in a sound file.
Overview of Method
Rather than looking at raw levels in the signal, we will first convert it to a sliding-window RMS. This gives a simple measure of how much audio energy is at any given point of the audio sample. By analyzing the RMS signal we can automatically determine a threshold for distinguishing between backgroun noise and speech.
Worked Example
I will be working this example in MATLAB because it makes the math easy to do and lets me create illustrations.
Source Audio
I am using President Kennedy's "We choose to go to the moon" speech. I'm using the audio file from Wikipedia, and just extracting the left channel.
imported = importdata('moon.ogg');
audio = imported.data(:,1);
plot(audio);
plot((1:length(audio))/imported.fs, audio);
title('Raw Audio Signal');
xlabel('Time (s)');
Generating RMS Signal
Although you could techinically implement an overlapping per-sample sliding window, it is simpler to avoid the overlapping and you'll get very similar results. I broke the signal into one-second chunks, and stored the RMS values in a new array with one entry per second of audio.
audioRMS = [];
for i = 1:imported.fs:(length(audio)-imported.fs)
audioRMS = [audioRMS; rms(audio(i:(i+imported.fs)))];
end
plot(1:length(audioRMS), audioRMS);
title('Audio RMS Signal');
xlabel('Time (s)');
This results in a much smaller array, full of positive values representing the amount of audio energy or "loudness" per second.
Picking a Threshold
The next step is to determine how "loud" is "loud enough." You can get an idea of the distribution of noise levels with a histogram:
histogram(audioRMS, 50);
I suspect that the lower shelf is the general background noise of the crowd and recording environment. The next shelf is probably the quieter applause. The rest is speech and loud crowd reactions, which will be indistinguishable to this method. For your application, the loudest areas will almost always be speech.
The minimum value in my RMS signal is .0233, and as a rough guess I'm going to use 3 times that value as my criterion for noise. That seems like it will cut off the whole lower shelf and most of the next one.
A simple check against that threshold gives a count of 972 seconds of speech:
>> sum(audioRMS > 3*min(audioRMS))
ans =
972
To test how well it actually worked, we can listen to the audio that was eliminated.
for i = 1:length(speech)
if(~speech(i))
clippedAudio = [clippedAudio; audio(((i-1)*imported.fs+1):i*imported.fs)];
end
end
>> sound(clippedAudio, imported.fs);
Listening to this gives a bit over a minute of background crowd noise and sub-second clips of portions of words, due to the one-second windows used in the analysis. No significant lengths of speech are clipped. Doing the opposite gives audio that is mostly the speech, with clicks heard as portions are skipped. The louder applause breaks also make it through.
This means that for this speech, the threshold of three times the minimum RMS worked very well. You'll probably need to fiddle with that ratio to get good automatic results for your recording environment, but it seems like a good place to start.