Pseudocode for confidence estimation about k2 HOT 5 OPEN

@danpovey

dense_fsas = k2fsa.dense_fsas(loglikes)

Are you implementing DenseFsaVec? How is a DenseFsaVec constructed from loglikes?

from k2.

Look in fsa.h. It is just a struct. Needs a constructor, that's all.

from k2.

Note, something would need to be done in Python in general, when constructing it, to add the - infinitys in the right place. (This needs to be done at the Python level, using the base toolkit, so that autograd will work).
There are 2 cases for construction: regular and irregular. In the irregular case, please note that Lhotse keeps track of the supervision start/end times separately from the features start/end times, so even if the features have all the same length the supervisions may not. We probably shouldn't interface directly with lhotse but should give it some reasonably usable interface. In general, each sequence (i.e. at the output of the nnet, a sequence of loglikelihoods) will be associated with zero or more supervision objects, possibly overlapping in time.

from k2.

Look in fsa.h. It is just a struct. Needs a constructor, that's all.

DenseFsaVec contains only two members: RaggedShape and Array2<float>.
Is it equivalent to the emssion graph in the Figure 2(d) of this paper?

lattices = k2fsa.pruned_compose(dense_fsas, decoding_graph)

Since dense_fsas contains ragged shape instead of FSAs, do we construct the FSAs dynamically during
intersection?

from k2.

from k2.