**• 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 called`frame_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