MESSAGE
| DATE | 2015-12-30 |
| FROM | Ruben Safir
|
| SUBJECT | Subject: [Hangout-NYLXS] Linux and voice recognition software
|
http://www.speech.cs.cmu.edu/sphinx/doc/Sphinx.html
Speech at CMU | Language Modelling Tool
Docs | Models | SphinxTrain | download | FAQ
CMU Sphinx
CMU Sphinx is a set of speech recognition development libraries and
tools that can be linked in to speech-enable applications. The libraries
and sample code can be used for both research and commercial purposes;
for instance, Sphinx2 can be used as a telephone-based recognizer, which
can be used in a dialog system. Sphinx3 is a slower, more accurate decoder.
Try a System
If you'd like to have a chance to try out an application that uses CMU
Sphinx, try the Communicator, an experimental system that helps you plan
air travel. You can reach it at the toll-free number 1-877-CMU-PLAN
(1-877-268-7526) or at +1 412 268 1084.
The system will provide real flight information. More complete
information about the Communicator can be found here. The system may be
sensitive to loud background noises, especially over cell phones.
Note that your call will be recorded for development purposes and may be
shared with other researchers. We don't have a policy set up yet for
placing such recordings into a publicly availably database, and so there
is no guarantee that this data will become publicly available -- though
we're motivated to set that up in the future.
The Sphinx Group
The Sphinx Group at Carnegie Mellon University is committed to releasing
the long-time, DARPA-funded Sphinx projects widely, in order to
stimulate the creation of speech-using tools and applications, and to
advance the state of the art both directly in speech recognition, as
well as in related areas including dialog systems and speech synthesis.
The licensing terms for the Sphinx engines and tools are derived from
BSD, and based, in particular, upon the license for the Apache web
server. There is no restriction against commercial use or
redistribution. (License terms for CMU Sphinx)
Automatic Speech Recognition
The task of an Automatic Speech Recognition (ASR) engine is to take
audio input and turn it into a written representation. This is sometimes
called "decoding," because you can think of the audio as an input
channel and the written form, whether it is a sequence of words or
speech sounds, as the output channel, with the decoder working between
them. Once the the speech has been converted into some text
representation, it can be used to send events to an application.
The ability to speak, and the ability to listen, co-evolved in humans;
the ear and the mouth are tuned to each other. A talker uses speech
sounds in order to be understood, and a listener draws on expectations
about speech when they listen.
Linguistic theory breaks speech down into a set of levels at which
language can be described: phonetics, phonology, morphonology, syntax,
semantics, pragmatics. It the first four of these that drive most speech
recognition systems, though the division between phonetics and phonology
is often ignored, and the speech sounds are referred to as "phones."
Speech recognition systems draw on these sources of knowledge, and find
the best combined result that finds the best sequence of sounds and
words that harmonize the phonetic models, the word models from a
pronunciation dictionary), and a "language model" or grammar that
predicts what word relationships are likely (or possible at all).
Most modern ASR systems use statistical methods to train models for each
of these levels, with linguistically informed choices. Hidden Markov
Models (HMMs), decision trees, and other statistical and machine
learning techniques are used throughout the system, and in speech
science as a whole.
Many systems do not explicitly use semantic information in the
recognizer, but leave that to a parser. Syntax may also be weakly
modeled, with a word sequence model (N-gram model) in the engine that
does not capture long distance relationships, or effects such as
subject-verb agreement directly.
A recognizer can often send the N-best decodes -- as an ordered list of
the best output text strings -- to the parser, and it can then find
which ones are syntactically or semantically consistent.
ASR is one component technology needed to build a speech-interactive
application. It may be used under the control of a VoiceXML interpreter,
or into a more general speech-interactive system. After the text comes
in, it may be passed to a parser, that would turn the input text strings
into a representation of the semantics of the message. A dialog manager
could then take that meaning input and perform some action in response,
sending its output intentions to a language generation component, which
would in turn pass this on to a speech synthesizer.
From Starting Out to Standing Up!
Sphinx is a large, complex codebase that was originally created using
"middle-out" development for research purposes. While it has grown into
the basis of many commercial systems, and the free release has led to a
lot of advances, it still can be intimidating to take the first steps.
The example code given is a good place to start, but it will take the
experience of building a real task using the toolkit, perhaps linking
the libraries in to your own applications and going beyond the simple
example apps (such as sphinx2-continuous) and doing new and interesting
things with speech technology.
Sphinx2
Sphinx2 is a fast, large vocabulary speaker independent recognition
system for continuous speech developed at Carnegie Mellon University. It
was released under a BSD-style License, which makes it free for both
commercial and non-commercial use. The license satisfies the Open Source
definition.
Download
You can download sphinx2 from SourceForge. The CVS tree and the releases
are available there.
Note: The tarball releases lag behind the CVS tree. Developers, please
check the latest version out of CVS. The following commands will check
it out. Just press the Enter key when the login asks for your password.
cvs
-d:pserver:anonymous-at-cvs.cmusphinx.sourceforge.net:/cvsroot/cmusphinx login
cvs -z3
-d:pserver:anonymous-at-cvs.cmusphinx.sourceforge.net:/cvsroot/cmusphinx co
sphinx2
Updates from within the module's directory do not need the -d parameter.
Bug Tracking and Discussion Groups
There are fora for bug tracking and discussions on the SourceForge site,
also. Please go there for help, questions, to report bugs, and to see
the latest work. The work is currently pre-version 1.0, so there is a
lot yet to be done.
Building Sphinx2
under construction. This section will contain build instructions for
Linux, Windows, Solaris, and Alpha platforms, and more as available.
Using Sphinx2
Running the examples
under construction. Here will be descriptions of the example code.
Build a Language Model
You can build your own language models for use with Sphinx using the
Language Modelling Tool web page. Just make a simple text file
containing sentences and words that you would like to have recognized,
and upload the file with your browser. You will see the progress as it
builds your files, and then it will present you with the final products.
Note: The LM Tool still builds for the OLD phoneset right now, with the
deletable stops and the TS phone. This will be updated shortly. If you
want to use it with the current Sphinx2 CVS version, just edit the
resulting .dic file to remove the D from PD TD KD BD DD GD (to make them
P T K B D G), and split the phone TS into T and S.
You may also want to use the CMU-Cambridge Statistical Language Modeling
Toolkit to build models, and get word pronunciations from the CMU
Dictionary.
Note: By default, Sphinx2 uses a slightly different phone set for
American English than is in the CMU Dictionary. Internally, Sphinx2 does
not use lexical stress; you need to run the CMU Dictionary through the
stress2sphinx utility included in the Sphinx2 release to convert it to
the default phone set.
Acoustic Models
There is a set of models for American English that comes with Sphinx2,
and there are also some other example acoustic models available.
You can use SphinxTrain to build acoustic models for any language and
any channel conditions.
How Sphinx works
needs to be fleshed out
Sphinx uses Hidden Markov Models to find the best path through the
combined constraints of the acoustic, lexical, and language model, given
the input audio.
Internally, Sphinx2 uses a fixed topology of 5-state phone models with
skips. Historically, these subphonetic states have been called senones
at CMU.
[ picture needed ]
Each of these 5-state models makes up a phone model. At run-time, the
system compares a frame of audio input to the distributions in each
senone, and finds the best path using viterbi decoding. Since the
realization of a phone is influenced by its neighbors (often called
coarticulation), one phone to the left and one to the right are used to
describe this context. A triphone is a phone with the left and right
context named. It is not three phones -- it is one context-dependent phone.
A pronunciation dictionary is used to find the relevant sequences of
phones, and the language model is used to find the probabilities of
sequences of words.
History
Lots of prior history needs to be filled in here
The Sphinx Group has been supported for many years by funding from the
Defense Advanced Research Projects Agency, and the recognition engines
to be released are those that the group used for the various DARPA
projects and their respective evaluations.
In early 2000, the Sphinx Group released Sphinx2, a real-time, large
vocabulary, speaker independent speech recognition system as free
software under the Apache-style license. Sphinx2 is the engine used in
the Sphinx Group's dialog systems that require real-time speech
interaction, such as the implementation of the DARPA Communicator
project, a many-turn dialog for travel planning. The pre-made acoustic
models include American English and French in full bandwidth, and
telephone-bandwidth Communicator models; Sphinx2 is a decent candidate
for handheld, portable, and embedded devices, and telephone and desktop
systems that require short response times.
Resources
Language Modeling
LMtool
You can build your own language models for use with Sphinx using the
Language Modelling Tool web page. Just make a simple text file
containing sentences and words that you would like to have recognized,
and upload the file with your browser. You will see the progress as it
builds your files, and then it will present you with the final products.
CMU/Cambridge SLM Toolkit
The CMU/Cambridge Statistical Language Modeling Toolkit is a
powerful suite of tools for building language models from text corpora.
Acoustic Modeling
SphinxTrain is a suite of training scripts and tools for training
acoustic models for CMU Sphinx. It was first publically released on June
7th, 2001. With this contribution, people should be able to build models
for any language and condition for which there is enough acoustic data.
Kevin Lenzo
--
So many immigrant groups have swept through our town
that Brooklyn, like Atlantis, reaches mythological
proportions in the mind of the world - RI Safir 1998
http://www.mrbrklyn.com
DRM is THEFT - We are the STAKEHOLDERS - RI Safir 2002
http://www.nylxs.com - Leadership Development in Free Software
http://www2.mrbrklyn.com/resources - Unpublished Archive
http://www.coinhangout.com - coins!
http://www.brooklyn-living.com
Being so tracked is for FARM ANIMALS and and extermination camps,
but incompatible with living as a free human being. -RI Safir 2013
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