Speed-up Facilities in s3.3 GMM Computation Seach Frame-Level Senone-Level Gaussian-Level Component-Level Not implemented SVQ-based GMM Selection Sub-vector.

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Presentation transcript:

Speed-up Facilities in s3.3 GMM Computation Seach Frame-Level Senone-Level Gaussian-Level Component-Level Not implemented SVQ-based GMM Selection Sub-vector constrained to 3 SVQ code removed Lexicon Structure Pruning Heuristic Search Speed-up Tree. Standard Not Implemented

Summary of Speed-up Facilities in s3.4 GMM Computation Seach Frame-Level Senone-Level Gaussian-Level Component-Level (New) Naïve Down-Sampling (New) Conditional Down-Sampling (New) CI-based GMM Selection (New) VQ-based GMM Selection (New) Unconstrained no. of sub- vectors in SVQ-based GMM Selection (New) SVQ code enabled Lexicon Structure Pruning Heuristic Search Speed-up Tree (New) Improved Word-end Pruning (New) Phoneme- Look-ahead

Near Term Improvement of Decoder  Improve LM facilities (Avail at Mar 31)  Improve speed-up techniques (Avail at Mar 31) Complete phoneme look-ahead research Complete machine optimization in Intel platform  Enable speed-up in live-mode recognition. (Avail at Mar 31)  Improved search structure Modify code to use lexical tree copies (Apr 15) Modify code to handle cross-word triphones (Apr 30)

Training Plan  Text-Processing (Avail at Mar 31)  First Pass of Acoustic/Language Modeling (Avail at Apr 15) With the help of the new 4 cpus machine. Training using standard recipe CD + CI mode first pass models. Trigram models.  Second Pass of Acoustic/Language Modeling Improved training.  Decide what we should do after we get the results.  AM/LM Adaptation? (Don ’ t know yet)