Re:Single Speaker Speech Recognition with Hidden Markov Models
by kastnerkyle on Friday May 23, 2014 @04:41AM
(#7788498)
You might be able to do this with independent component analysis (ICA). There is a block in sklearn, but you would have to write some Python.
Alternatively, you might look to see if the speakers are in separate channels (if recording is stereo). Then it would be really simple - just take one channel out and resave as mono!
If you have a sample I'd be curious to take a look - sounds like an interesting problem.
Re:Single Speaker Speech Recognition with Hidden Markov Models
by relate on Friday May 23, 2014 @05:55AM
(#7788645)
ICA helps you separate a superposition of two or more signals. Assuming the speakers are not speaking at the same time, he might rather want to use speaker identification methods to find what parts to mute (e.g. train a binary classifier that operates a spectrogram of the signal).
Re:Single Speaker Speech Recognition with Hidden Markov Models
by kastnerkyle on Monday May 26, 2014 @02:05AM
(#7798876)
Good point. I was thinking of two people speaking at the same time, but that would probably be hard to listen to :). Speaker dependent muting is a much more reasonable approach
I've got an audiobook with a particularly obnoxious commentator interjecting dumb things every couple of minutes and I'd like to slice those out.
I'd do it manually if it were only a couple of edits, but there are easily 150+ comments spread through 6 hrs of material. Should be easy for a speech recognizer, just two voices with different genders.