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--- tips:speech:simon [2017/03/05 08:45] – [model] scipio
+++ tips:speech:simon [2017/03/14 20:59] (current) – [compile] scipio
@@ Line 1: / Line 1: @@
 ====== simon ======
+speech model basically consists of two parts:
+  * Language model: Describes all existing words and what sentences are grammatically correct. It will be created from your active scenario
+  * Acoustic model: Describes how words sound. It will be either built solely through your voice recordings (training) or with the help of a base model
+SPHINX backend" uses:
+  * PocketSphinx recognizer
+  * SphinxTrain model generation backend
+models:
+  * US English HUB4 WSJ SPHINX model. This model is part of pocketsphinx-extra where it is called \"hub4_wsj_sc_3s_8k.cd_semi_5000\".
+===== install sphinx =====
+prereq
+<code bash>
+sudo apt install bison gradle openjdk-8-jdk
+</code>
+build from sources http://cmusphinx.sourceforge.net/wiki/download, not use ubuntu packages
 ===== install =====
 <code bash>
 sudo apt install simon
-sudo apt install libsphinxbase3 python-sphinxbase sphinxbase-utils pocketsphinx
+sudo apt install libsphinxbase3 python-sphinxbase sphinxbase-utils pocketsphinx sphinxtrain
 </code>
@@ Line 14: / Line 40: @@
 </file>
+===== compile =====
+https://phabricator.kde.org/source/simon/
+<code bash>
+sudo apt-get build-dep simon
+git clone git://anongit.kde.org/simon
+cd simon
+export QT_VERSION=qt4
+rm -fR build
+./build_ubuntu.sh
+</code>
 ==== model ====
+=== adapting ===
+http://cmusphinx.sourceforge.net/wiki/tutorialadapt
+<code bash>
+sudo apt install pocketsphinx-en-us/xenial
+</code>
+<code bash>
+mkdir /tmp/model
+cd /tmp/model
+cp -a /usr/share/pocketsphinx/model/en-us/en-us .
+cp /usr/share/pocketsphinx/model/en-us/cmudict-en-us.dict .
+cp /usr/share/pocketsphinx/model/en-us/en-us.lm.bin .
+</code>
+=== training ===
+training from http://cmusphinx.sourceforge.net/wiki/tutorialamdown
+* download [[model|http://www.speech.cs.cmu.edu/databases/an4/an4_sphere.tar.gz]]
+* enter and sphinxtrain -t an4 setup
+==== old ====
 For small amount of data for training without context-dependent models use continuous model instead of semi-continuous one and this error will be solved.