||Are Log Negative Probability, so a higher cost means lower probability.
||Each 10ms of audio that using MFCC turned into a fixed size vector called a frame.
||Cutoff would be
Beam (Around 10 to 16)
||The maximum cost that all cost higher than this value will not be processed and removed.
||The zero label in
FST are called
||Are the same as FSTs, instead each token keeps in a framed based array called
frame_toks. In This way the distance in time between each token will be perceived too.
||A language model scoring system that applied after final state to improve final result by using stronger LM model than
||The main FST used in the decoding. The iLabel in this FST is TransitionIDs.
||A model that used to convert sound into acoustic cost and TransitionIDs.
||A number that contain information about state and corresponding PDF id.
|• Emiting States:
||States that have pdfs associated with them and emit phoneme. In other word states that have their
ilabel is not zero
|• Bakis Model:
||Is a HMM that state transitions proceed from left to right. In a Bakis HMM, no transitions go from a higher-numbered state to a lower-numbered state.
|• Max Active:
|| Uses to calculate cutoff to determince maximum number of tokens that will be processed inside emitting process.
|• Graph Cost:
||is a sum of the LM cost, the (weighted) transition probabilities, and any pronunciation cost.
|• Acoustic Cost:
||Cost that is got from the decodable object.
|• Acoustic Scale:
||A floating number that multiply in all Log Likelihood (inside the decodable object).
Fig. 1. Demonstration of Finite State Automata vs Lattices, Courtesy of Peter F. Brown
- Stanford University – Speech and Language Processing Book
- IEEE ICASSP – Partial traceback and dynamic programming