• 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 CostBeam (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

  1. Stanford University – Speech and Language Processing Book
  2. IEEE ICASSP – Partial traceback and dynamic programming

Leave a Reply

Your email address will not be published.