Let’s Enhance Kaldi, Here are some links along the way. Look like YouTube is progressing a lot during the last couple of years so basically here is just a bunch of random videos creating my favorite playlist to learn all the cool stuff under the Kaldi’s hood.
Lattices: A more complex form of
FST‘s, The first version decoders were based on FST’s (like
online decoders). For Minimum Bayesian Risk Calculation Using
Lattices will give you a better paved way
faster-decoder: Old decoder, very simple to understand how decoding process is done
lattice-faster-decoder: general decoder, same as
faster-decoder but output lattices instead of
DecodableInterface: An interface that connects decoder to the features. decoder uses this
Decodable object to pull CMVN features from it.
BestPath: An FST that constructed from the Best Path (path with maximum likelihood) in the decoded FST.
nBestPath: An FST constructed from the top N Best Path in the decoded FST.
GetLinearSymbolSequence: The final step in the recognition process, get a BestPath FST or Lattice and output the recognized words with the path weight.
CompactLattices need to be converted using
Strongly Connected Component: A set that all components are accessible (in two ways) by it’s member.
ProcessEmitting that pulls
loglikelihood from the