| • Costs: |
Are Log Negative Probability, so a higher cost means lower probability. |
| • Frame: |
Each 10ms of audio that using MFCC turned into a fixed size vector called a frame. |
| • Beam: |
Cutoff would be Best Cost–Beam (Around 10 to 16) |
| • Cutoff: |
The maximum cost that all cost higher than this value will not be processed and removed. |
| • Epsilon: |
The zero label in FST are called <eps> |
| • Lattices: |
Are the same as FSTs, instead each token keeps in a framed based array calledframe_toks. In This way the distance in time between each token will be perceived too. |
| • Rescoring: |
A language model scoring system that applied after final state to improve final result by using stronger LM model than n-gram. |
| • HCLG(FST): |
The main FST used in the decoding. The iLabel in this FST is TransitionIDs. |
| • Model(MDL): |
A model that used to convert sound into acoustic cost and TransitionIDs. |
| • 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
