Miroirs/slackbuilds_ponce
1
0
Fork 0
mirror of https://github.com/Ponce/slackbuilds synced 2024-11-22 19:44:21 +01:00
Code Issues Projects Releases Packages Wiki Activity Actions

development/julius: Fix README.

Browse source 
Signed-off-by: B. Watson <yalhcru@gmail.com>

Signed-off-by: Willy Sudiarto Raharjo <willysr@slackbuilds.org>
This commit is contained in:
B. Watson 2020-10-13 00:23:59 -04:00 committed by Willy Sudiarto Raharjo
parent 99722947e4
commit 271207d332
No known key found for this signature in database
GPG key ID: 3F617144D7238786
1 changed files with 13 additions and 12 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

25
development/julius/README
View file

@ -1,12 +1,13 @@
"Julius" is a high-performance, two-pass large vocabulary continuous speech
recognition (LVCSR) decoder software for speech-related researchers and
developers. Based on word N-gram and context-dependent HMM, it can perform
almost real-time decoding on most current PCs in 60k word dictation task.
Major search techniques are fully incorporated such as tree lexicon, N-gram
factoring, cross-word context dependency handling, enveloped beam search,
Gaussian pruning, Gaussian selection, etc. Besides search efficiency, it
is also modularized carefully to be independent from model structures, and
various HMM types are supported such as shared-state triphones and
tied-mixture models, with any number of mixtures, states, or phones.
Standard formats are adopted to cope with other free modeling toolkit such
as HTK, CMU-Cam SLM toolkit, etc.
"Julius" is a high-performance, two-pass large vocabulary continuous
speech recognition (LVCSR) decoder software for speech-related
researchers and developers. Based on word N-gram and context-dependent
HMM, it can perform almost real-time decoding on most current
PCs in 60k word dictation task. Major search techniques are fully
incorporated such as tree lexicon, N-gram factoring, cross-word context
dependency handling, enveloped beam search, Gaussian pruning, Gaussian
selection, etc. Besides search efficiency, it is also modularized
carefully to be independent from model structures, and various HMM
types are supported such as shared-state triphones and tied-mixture
models, with any number of mixtures, states, or phones. Standard
formats are adopted to cope with other free modeling toolkit such as
HTK, CMU-Cam SLM toolkit, etc.