document: Note that the presented architecture works at the frame level, meaning that each single frame (plus its corresponding context) is fed-forward through the network, obtaining a class posterior probability for all of the target languages. This fact makes the DNNs particularly suitable for real-time applications because, unlike other approaches (i.e. i-vectors), we can potentially make a decision about the language at each new frame. Indeed, at each frame, we can combine the evidence from past frames to get a single similarity score between the test utterance and the targetlanguages. A simple way of doing this combination is to assume that frames are independent and multiply the posterior estimates of the last layer. The score sl for language l of a given test utterance is computed by multiplying the output probabilities pl obtained for all of its frames; or equivalently, accumulating the logs as:(6)sl=1N∑t=1Nlogp(Ll|xt​, θ)where p(Ll|xt​, θ) represents the class probability output for the language l corresponding to the input example at time t, xt by using the DNN defined by parameters θ.
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I I I I I O O O O O O O O B I O O O O O O O O O O O O B I I I I I I I I I I I I I O O O O O O O O O B I O O O B I I I I I I I I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O
extractive_keyphrases: assume that frames are independent and multiply the posterior estimates of the last layer combine the evidence from past frames dnn dnns multiplying the output probabilities pl obtained for all of its frames other approaches target languages test utterance
abstractive_keyphrases: accumulating the logs as:(6)sl=1n∑t=1nlogp(ll|xt​, θ) evidence from past frames i-vectors