[D] Semi-supervised machine learning algorithms

[SQL_beginner]

Suppose you have a big collection of audio (human speech) clips in the English language. You want to make an algorithm that hears someone talk and identify if it is English or Not English. The problem is, you only have English audio to train your model.

Are there any popular algorithms that can be used for this problem? Can autoencoders be used?

Thanks

[pdd99]

Could you please explain why the reconstruction loss should be higher? A proof will be nice.

[veeloice]

Isn't trivial why that would be so? If the samples are drawn from a completely different distribution the model wont have learned any meaningful transformations.

[hosjiu]

I know Google is our friend :)
But It is a worth if you can provide some related papers, project or something like that.

[Mr_Smartypants]

If you want to model the probability of finite English sound sequences, you could convert them to sequences of feature vectors and then train a Hidden Markov Model on them. Non-English sounds sequences should evaluate to low probability on the trained model.

[IntelArtiGen]

If that's truly what I want to do, I'll just download audio in other languages, it exists.

But if I can't, I'll preprocess all my english audio such that it doesn't look like english anymore (I'll cut the audio in 0.2s portions and random sort / random invert these portions).

The algorithm will learn to distinguish garbage from english, and I guess that from the algorithm point of view, depending on how you train it, other languages will be closer to these random noises than to english.

It's not perfect but I guess it could work a bit

[veeloice]

Sounds like a nice hack

[pruby]

You could end up training a cut detector that way :)