Building Language Model
构建语言模型
There are two types of models that describe language - grammars and statistical language models. Grammars describe very simple types of languages for command and control, and they are usually written by hand or generated automatically
with plain code.
有两种描述语言模型 - 语法和统计语言模型，语法语言模型描述像命令和控制语言这样很简单的语言类型，它们通常是通过手写或者明码自动产生。
There are many ways to build the statistical language models. When your data set is large, there is sense to use CMU language modeling toolkit. When a model is small, you can use an online quick web service. When you need specific
options or you just want to use your favorite toolkit which builds ARPA models, you can use it.
有很多方法来构建统计语言模型，当你的数据集很大的时候，使用CMU语言模型工具是很有帮助的；当模型很小时，可以使用快速在线web服务；你可以使用特殊选项或使用你喜欢的工具来构建ARPA模型。
Building a grammar
构建语法
Grammars are usually written manually in JSGF format:
JSGF格式的语法通常手写为：
#JSGF V1.0;
/**
* JSGF Grammar for Hello World example
*/
grammar hello;
public <greet> = (Good morning | Hello) ( Bhiksha | Evandro | Paul | Philip | Rita | Will );
For more information on JSGF format see the documentation
关于JSGF格式的详细信息见以下文档
Building a Statistical Language Model Using CMUCLMTK
使用CMUCLMTK来构建语言模型
Required Software
软件要求
You need to download and install cmuclmtk. See
CMU Sphinx Downloads for details.
需要下载安装cmuclmtk，详细见CMU Sphinx下载页。
Text preparation
First of all you need to cleanup text. Expand abbreviations, convert numbers to words, clean non-word items. For example to clean WikipediaXML dump you can use special python
scripts. To cleanHTML pages you can tryhttp://code.google.com/p/boilerpipe/
a nice package specifically created to extract text from
HTML
首先需要清理文本，扩展缩写，转换数字为单词，清除非单词项，比如可以使用python脚本清理维基百科的XML，为了清理HTML网页，可以使用http://code.google.com/p/boilerpipe/，一个专门用来从HTML中提取文本的包。
For example on how to create language model from Wikipedia texts please see
比如，如何从维基百科文本创建语言模型，请看
http://trulymadlywordly.blogspot.ru/2011/03/creating-text-corpus-from-wikipedia.html
Once you went through the language model process, please submit your langauge model to CMUSphinx project, we'd be glad to share it!
当经过训练语言模型的过程，请提交你的语言模型到CMUSphinx项目，我们很乐意分享它！
Language modeling for Mandarin is largely the same as in English, with one addditional consideration, which is that the input text must be word segmented. A segmentation tool and associated word list is provided to accomplish
this.
普通话语言模型和英语语言模型一样大，另一需要注意的是，输入的文本必须是已经分割好的词，分割工具和相关单词列表已经提供。
ARPA model training
APRA模型训练
The process for creating a language model is as follows:
下面是创建语言模型的过程：
1) Prepare a reference text that will be used to generate the language model. The language model toolkit expects its input to be in the form of normalized text files, with utterances delimited by<s> and</s>
tags. A number of input filters are available for specific corpora such as Switchboard, ISL and NIST meetings, and HUB5 transcripts. The result should be the set of sentences that are bounded by the start and end sentence markers: <s> and </s>. Here's an example:
1）准备用于产生语言模型的参考文本，语言模型工具需要输入的是标准形式的文本文件，对应的语料用<s>和</s>标签来分割，有很多滤波器可用于具体的语料，这些预料包括电话总机的、路由器、NIST会议和HUB5文本，句子集要用<s>和</s>句子标记来界定句子的开始和结束位置，下面是一个例子：
<s> generally cloudy today with scattered outbreaks of rain and drizzle persistent and heavy at times </s>
<s> some dry intervals also with hazy sunshine especially in eastern parts in the morning </s>
<s> highest temperatures nine to thirteen Celsius in a light or moderate mainly east south east breeze </s>
<s> cloudy damp and misty today with spells of rain and drizzle in most places much of this rain will be
light and patchy but heavier rain may develop in the west later </s>
More data will generate better language models. Theweather.txt file from sphinx4 (used to generate the weather language model) contains nearly 100,000 sentences.
数据越多会产生更好的语言模型，sphinx4的weather.txt文件(用于产生天气语言模型)包含将近100000个句子。
2) Generate the vocabulary file. This is a list of all the words in the file:
2) 产生词汇文件，这是一个列表的文件中的所有单词：
text2wfreq < weather.txt | wfreq2vocab > weather.tmp.vocab
3) You may want to edit the vocabulary file to remove words (numbers, misspellings, names). If you find misspellings, it is a good idea to fix them in the input transcript.
3) 你可以编辑词汇文件来去除单词(数字、拼写错误、名字)，如果发现拼写错误，最好能在输入文本中就改正它们。
4) If you want a closed vocabulary language model (a language model that has no provisions for unknown words), then you should remove sentences from your input transcript that contain words that are not in your vocabulary file.
4) 如果你需要一个自己的语言模型(一个无规则的未知单词语言模型)，应该从输入文本中移除句子，输入文本包含单词，这些单词并不在词汇文件中。
5) Generate the arpa format language model with the commands:
5) 产生arpa格式的语言模型的命令：
% text2idngram -vocab weather.vocab -idngram weather.idngram < weather.closed.txt
% idngram2lm -vocab_type 0 -idngram weather.idngram -vocab \
weather.vocab -arpa weather.arpa
6) Generate the CMU binary form (DMP)
6) 产生CMU二进制文件
sphinx_lm_convert -i weather.arpa -o weather.lm.DMP
The CMUCLTK tools and commands are documented at
The CMU-Cambridge Language
Modeling Toolkit page.
CMUCLTK工具和命令记录在The CMU-Cambridge Language Modeling Toolkit page中
Using other Language Model Toolkits
使用其他语言模型工具
You can also use any other toolkit that generates ARPA text files. However, the resulting files must be sorted in order to work with the Sphinx decoders. You can use thesphinx_lm_sort utility included with SphinxBase
to sort an ARPA format language model file for use with Sphinx, e.g.:
你也可以使用其他任何工具产生的ARPA文本文件，可是，结果文件必须要进行分类以便和Sphinx编码器一起工作，你可以使用包含SphinxBase库的sphinx_lm_sort工具来分类ARPA格式的语言模型文件用于Sphinx，例如:
sphinx_lm_sort < unsorted.arpa > sorted.arpa
Then you can convert the model to DMP format and use it as usual.
你可以将模型转换为DMP格式，然后像平常一样使用。
Some toolkits you can try:
有些工具你可以试试：
They are usually pretty convenient to use.
它们使用通常很方便。
Building a simple language model using web service
使用web服务器来构建一个简单语言模型
If your language is English and text is small it's sometimes more convenient to use web service to build it. Language models built in this way are quite functional for simple command and control tasks. First of all you need to
create a corpus.
如果你的语言是英语，文本很小，使用web服务器来构建它是非常方便的，用这种方式来构建的语言模型，对简单命令和控制任务是非常有效的，首要任务是建立一个语料库。
The “corpus” is just a list of sentences that you will use to train the language model. As an example, we will use a hypothetical voice control task for a mobile Internet device. We'd like to tell it things like “open browser”,
“new e-mail”, “forward”, “backward”, “next window”, “last window”, “open music player”, and so forth. So, we'll start by creating a file calledcorpus.txt:
这里的“语料库”仅仅是句子列表，用来训练语言模型的。举个例子，我们使用声控任务来操作一台移动互联设备，我们准备告诉它类似于这样的事情，“打开浏览器”、“新建邮件”、“前进”、“后退”、“下一个窗口”、“最后窗口”、“打开音乐播放器”等等，因此，我们开始新建一个文件corpus.txt：
open browser
new e-mail
forward
backward
next window
last window
open music player
Then go to the page http://www.speech.cs.cmu.edu/tools/lmtool-new.html.
Simply click on the “Browse…” button, select the corpus.txt file you created, then click “COMPILE KNOWLEDGE BASE”.
转到http://www.speech.cs.cmu.edu/tools/lmtool-new.html页面，单击“浏览...”按钮，选择创建的corpus.txt文件，然后单击“COMPILE
KNOWLEDGE BASE”。
The legacy version is still available online also here:http://www.speech.cs.cmu.edu/tools/lmtool.html
旧版本在网上仍然可以在这 http://www.speech.cs.cmu.edu/tools/lmtool.html 获得
You should see a page with some status messages, followed by a page entitled “Sphinx knowledge base”. This page will contain links entitled “Dictionary” and “Language Model”. Download these files and make a note of their names
(they should consist of a 4-digit number followed by the extensions .dic and.lm). You can now test your newly created language model with PocketSphinx.
你应该看一看“Sphinx knowledge base”网页信息，这个网页包含“字典”和“语言模型”的链接，下载这些文件并记录它们的名字(它们应该是由4个数字的扩展组成)，你现在可以使用PocketSphinx来测试一下新建立的语言模型了。
Converting model into DMP format
将模型转换成DMP格式
To quickly load large models you probably would like to convert them to binary format that will save your decoder initialization time. That's not necessary with small models. Pocketsphinx and sphinx3 can handle both of them with-lm
option. Sphinx4 requires you to submit DMP model into TrigramModel component and ARPA model to SimpleNGramModel component.
为了快速加载大型的模型，最好可以将模型转换为二进制格式，这样可以节省解码器的初始化时间，对小型模型是没有必要这样做的，PocketSphinx和sphinx3使用-lm选型都可以处理它们，Sphinx4要求DMP模型符合3-gram模型组件，ARPA模型符合简单N-gram模型组件。
ARPA format and DMP format are mutually convertable. You can produce other file withsphinx_lm_convert command from sphinxbase:
ARPA格式和DMP格式是可以相互转换的，你可以用sphinx_lm_convert命令从sphinxbase产生其他文件。
sphinx_lm_convert -i model.lm -o model.dmp
sphinx_lm_convert -i model.dmp -ifmt dmp -o model.lm -ofmt arpa
Using your language model
使用语言模型
This section will show you how to use, test, and improve the language model you created.
这一节将展示如何使用、测试、改进创建的语言模型。
Using your language model with PocketSphinx
PocketSphinx使用语言模型
If you have installed PocketSphinx, you will have a program called
pocketsphinx_continuouswhich can be run from the command-line to recognize speech. Assuming it is installed under/usr/local, and your language model and dictionary are called8521.dicand8521.lm, try running the following command:如果你安装了PocketSphinx，将有一个pocketsphinx_continuous程序，这个程序可以从命令行运行来识别语音，假设它被安装在user/local，语言模型和字典是8521.dic和8521.lm，试着运行以下命令：
pocketsphinx_continuous -lm 8521.lm -dict 8521.dicYou will see a lot of diagnostic messages, followed by a pause, then “READY…”. Now you can try speaking some of the commands. It should be able to recognize them with complete accuracy.If not, you may have problems with your microphone or sound card.
你会看到很多特征信息，后跟一个暂停，然后显示“READY...”。现在你可以尝试说出一些命令，它应该能够准确完整的识别出来，如果不能，可能是你的麦克风或声卡有些问题。
Using your language model with Sphinx4
Sphinx4使用语言模型
You just need to edit the configuration file and put a proper file name there. Sphinx-4 mostly works with DMP format. See the documentation for details:
需要编辑配置文件，并写入合适的文件名称，Sphinx-4大多数是使用DMP格式来工作的，详细见文档：
http://cmusphinx.sourceforge.net/sphinx4/doc/UsingSphinxTrainModels.html