Sphinx 3.X (X=4)

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Sphinx 3.X (X4)
Four-Layer Categorization Scheme of Fast GMM
Computation Techniques in Large Vocabulary
Continuous Speech Recognition Systems
Arthur Chan, Jahanzeb Sherwani, Ravishankar
Mosur and Alex Rudnicky
Computer Science Department, Carnegie Mellon
Unversity
Sphinx 3.X (X4)
Sphinx -speaker-independent large vocabulary
speech recognition system -open source under
Berkeleys style license one can distribute,
modify and use it freely Sphinx 3.X
Reengineering of Sphinx 3 to create a real-time
large vocabulary speech recognizer -S3.3 Tree
lexicon, Histogram Pruning and Live-mode decoder
(R. Mosur 1999) -S3.4 (released Jul 04) Fast GMM
Computation , Phoneme lookahead. (A. Chan 2004,
this paper.) -S3.5 (will soon release)
MLLR-based Speaker Adaptation , live-mode APIs,
alignment, phoneme recognition, lattice
rescoring, best path search in lattice.
4-Level of GMM Computation
GMMs
Frames
Feature Component
Gaussian-level -VQ-based Gaussian Selection
(Bochierri 93) -SVQ-base Gaussian Selection
(Mosur 99)
Feature-level -Sub vector quantization or SDCHMM
method (Mosur 97 Mak 97) -LDA, PCA
Frame-Level -Discount alternative frames -Down
Sampling (Wycesna 95)
GMM-Level -Only compute important GMM. (e.g. w
high CI score) -CI-GMM Selection (Lee 01)
Sphinx 3.4 Fast GMM Computation
Experiment Results
Our approach -Divide GMM computation in 4
levels -Implement representative techniques in
each level -Inspired by 4-level state tying
(Sagayama 95) Observation In each level, full
computation can be approximated by computing only
parts of the components. Advantages Provide a
general framework of understanding fast GMM
computation Experiment Summary 1, CI-based GMM
Selection seems to most effective. 2, Many
Gaussian-level seems to have too much overhead
Algorithm WER Total GMM Srch Ovhd
BL 18.65 6.9 5.85 0.85 -
Down Sampling 19.10 4.35 3.99 0.96
CIGMMS 18.82 3.25 1.18 2.06 -
Gaussian Selection 18.95 3.95 2.84 0.89 0.22
SVQ 18.69 4.20 2.04 0.98 1.08
Note Results combined with pruning can be found
in the paper.