9 – Segmentally Boosted HMMs

In your past work on gesture recognition, how many dimensions have you used for your output probabilities? >> Up to hundreds. At one point, we are creating appearance models of the hand, using a similarity metric of how closely the current hand looked like different visual models of the hand as features for the HMM. … Read more

6 – Context Training

OK. Now let’s talk about another trick. When we moved from recognizing isolated signs to recognizing phrases of signs, the combination of movements looks very different. >> For example, when Thad signed NEED in isolation, his hands started from a rest position and finished in the rest position. When he signs NEED in the context … Read more

4 – Phrase Level Recognition

Now that we have topologies for our six signs, let’s talk about phrase level sign language recognition. We have eight phrases we want to recognize. >> Actually, you mean 7 signs and 12 phrases. >> Since we have two variants of cat we are recognizing, expanding all the possibilities leads to 12 phrases. >> Good … Read more

2 – Using a Mixture of Gaussians

What if our output probabilities aren’t Gaussian? >> Well according to the central limit theorem, we should get Gaussians if enough factors are affecting the data. >> But in practice sometimes the output probabilities really are not Gaussian. It is not hard for them to be bimodal. >> You mean like this. >> Yep. >> … Read more