Sphinx-4 contains a number of demo programs. If you downloaded the binary
distribution (sphinx4-{version}-bin.zip), the JAR files of the demos are already
built, so you can just run them directly. However, if you downloaded the source
distribution (sphinx4-{version}-src.zip or via CVS), you need to build the
demos. Click on the links below for instructions on how to build and run the
demos.
- Sphinx-4 Whitepaper
Sphinx-4:
A Flexible Open Source Framework for Speech Recognition describes the
framework and implementation of Sphinx-4 from a speech-technologist's
perspective. Please read this if you'd like to extend Sphinx-4.
- Frequently asked questions about
Sphinx-4
The document Frequently
asked questions about Sphinx-4 contains answers to a number of frequently
asked questions about Sphinx-4
- Understanding Sphinx-4 Configuration
Management
The document Sphinx-4
Configuration Management describes, in detail, how to configure a Sphinx-4
system.
- Understanding Sphinx-4 Instrumentation
The document Sphinx-4
Instrumentation describes, in detail, how to use the instrumentation
facilities of the Sphinx-4 system.
- Running the Regression Tests
Sphinx-4 contains a number of regression tests using common speech
databases. Again, you have to download the source distribution or downloaded
the source tree using CVS in order to get the regression tests directory. The
regression tests we have are:
Before you run any of the tests, make sure that you have built Sphinx-4
already. To do so, go to the top level and type:
ant
You also need to make sure you have the appropriate acoustic model(s)
installed. More details below.
The Sphinx-4 regression tests have different directories for the different
tasks. The directory sphinx4/tests/performance contains directories named
ti46, tidigits, an4, rm1, hub4, and some other tests. Each of these
directories contains a build.xml with targets specific to the particular task.
The build.xml allows you to run a number of different tests. Type:
ant -projecthelp
to list a help text with the possible targets.
Isolated Digits - TI46
The TIDIGITS models are already included as part of the distribution.
Therefore, you do not need to download them separately. You must have the TI46
test data, available from the LDC
TI46 website.
You need to edit the batch file called ti46.batch, located in
tests/performance/ti46 directory. You will need to change it such
that it matches where you stored the TI46 test files. Refer to the section Batch Files
for detail about the format of batch files.
To run the tests:
% cd sphinx4/tests/performance/ti46
% ant -projecthelp # to see a list of possible targets
% ant ti46_wordlist
Connected Digits - TIDIGITS
The TIDIGITS models are already included as part of the distribution.
Therefore, you do not need to download them separately.
You must have the TIDIGITS test data, available from the LDC
TIDIGITS website.
You need to edit the batch file called tidigits.batch, located
in the tests/performance/tidigits directory. You will need to
change it such that it matches where you stored the TIDIGITS test files. Refer
to the section Batch Files
for detail about the format of batch files.
To run the tests:
% cd sphinx4/tests/performance/tidigits
% ant -projecthelp # to see a list of possible targets
% ant tidigits_flat_unigram
Small Vocabulary - AN4
The Wall Street Journal (WSJ) models are already included as part of the
distribution. Therefore, you do not need to download them separately.
Download the big endian raw audio format of the AN4 Database. Unpack it
at a directory of your choice:
% gunzip an4_raw.bigendian.tar.gz
% tar -xvf an4_raw.bigendian.tar
Then update the following batch files (located in the
tests/performance/an4 directory), so that they match up with
where you unpacked the AN4 data. You probably just need to replace all
instances of the string "/lab/speech/sphinx4/data" inside these
batch files. Please refer to the Batch Files
section for details about batch files:
an4_full.batch
an4_spelling.batch
an4_words.batch
After you have updated the batch files, you can run the tests by:
% cd sphinx4/tests/performance/an4
% ant -projecthelp # to see a list of possible targets
% ant an4_words_unigram
Medium Vocabulary - RM1
Make sure that you have downloaded the binary RM1 model file, called
RM1_13dCep_16k_40mel_130Hz_6800Hz.jar, located at the
sphinx4 package in the downloads
page.
Then in the build file for the RM1 tests,
sphinx4/tests/performance/rm1/build.xml, changed the
classpath property of the build file to point to the location of
your RM1_13dCep_16k_40mel_130Hz_6800Hz.jar.
You must have the RM1 test data, available from the LDC
RM1 website.
You also need to prepare a batch file called rm1.batch, by
following instructions in the Batch Files
section. There is already one in the RM1 test directory, but it will not work
for you, since the paths to test files will not match your setup.
To run the tests:
% cd sphinx4/tests/performance/rm1
% ant -projecthelp # to see a list of possible targets
% ant rm1_bigram
Large Vocabulary - HUB4
You must have the HUB4 test data, available from the LDC
HUB4 website.
You must download the binary HUB4 model file, called
HUB4_8gau_13dCep_16k_40mel_133Hz_6855Hz.jar, and the binary HUB4
trigram language model, called HUB4_trigram_lm.zip, both located
at the sphinx4 package in the downloads
page. For the trigram language model file, unpack it by:
jar xvf HUB4_trigram_lm.zip
The trigram model file is called
language_model.arpaformat.DMP. Then, in the build file for the
HUB4 tests, sphinx4/tests/performance/hub4/build.xml, changed the
classpath property of the build file to point to the location of
your HUB4_8gau_13dCep_16k_40mel_133Hz_6855Hz.jar. In the
configuration file, tests/performance/hub4/hub4.config.xml,
change the 'location' of the 'trigramModel' component to where your
language_model.arpaformat.DMP file is located.
You also need to prepare a batch file, which is currently called
f0_hub4.batch in the build.xml file, by following instructions in
the Batch
Files section.
To run the test:
% cd sphinx4/tests/performance/hub4
% ant -projecthelp # to see a list of possible targets
% ant hub4_trigram
- Setting up a Regression Test
Each batch mode regression test consists of the following components:
- Test
data - the audio or cepstral data to perform the test on. This is
usually some well known database such as TIDIGITS or HUB-4. Alternatively,
it can also be data that you recorded on your own.
- Batch
File - this text file lists the location of all the test files, as well
as the transcription of the test file.
- Acoustic model & Dictionary
- Configuration file - specifies the configuration of the system you use
to test the data.
- Grammar file - this can either be a word list file, N-gram language
model, or a BNF-style grammar file (such as JSGF).
- Batch-mode
Recognizer - this is the Sphinx-4 batch-mode recognizer.
To learn about how to setup a regression test, take a look at the walkthrough
of setting up the AN4 tests.
Batch Files
Batch files are used in batch mode regressions tests. It is a text file
that contains the list of files to be processed, with the transcription for
each file. The format is as shown below: one line for each file, where the
first element in a line is the file name, which can be an absolute or relative
path, and includes the file extension; after the file name, the words that
make up the transcription for the audio. Sphinx-4 uses the transcription
provided here to compute the system's accuracy after each sentence is
processed. An utterance's processing produces in a hypothesis for what was
said. This hypothesis is compared with the transcription, i.e., the hypothesis
is aligned against the reference transcript, and a summary of the results is
reported.
/lab/speech/sphinx4/data/tidigits/test/raw16k/man/man.ah.24z982za.raw two four zero nine eight two zero
/lab/speech/sphinx4/data/tidigits/test/raw16k/man/man.ah.25896o4a.raw two five eight nine six oh four
An example batch file is tidigits.batch
(this link only works if you downloaded the source distribution).
Input Audio/Cepstral Files
The audio files used by Sphinx-4 can contain raw audio or cepstra, which is
a form of encoded speech. The Java platform has support for other data
formats, such as MS WAV or Sun's au, but, provided as is, Sphinx-4 can handle
only raw data.
The audio defaults to 2 bytes/sample, at 16000 samples per second. The
files are expected to be binaries without header. The Java platform assumes
big endian order, always. These defaults can be changed. For example, the byte
order or the sampling rate can be changed.
The input can also be cepstra. The cepstral file has a 4 byte integer
containing the number of floats that follow. The following floats are 13
dimensional vectors concatenated. Notice that since the first piece of
information is the number of floats, the total file size can be computed. If a
comparisons with the actual size fails, either the byte order has to be
reversed, or the file is corrupted. Importantly, the byte order can be
automatically detected.
Walkthrough of Setting up the AN4
Tests
To illustrate the process of setting up a regression test, lets use AN4, an
existing test, as an example. Use the following steps to create the AN4 tests.
- Create a test directory - the various files for each test set
(e.g., config file, batch file, grammar files, etc..) should reside in its
own directory, normally under
tests/performance. For example,
the AN4 tests reside in tests/performance/an4.
- Obtain and convert the test database - download the AN4 test
database from the AN4
website (choose "Raw audio (.raw) format, big endian byte order").
Unpack the downloaded tarball into a directory of your choice, which in our
case is
/lab/speech/sphinx4/data/an4. Since the AN4 test data
already comes in raw audio format, no conversion is necessary. However,
other test databases might require conversion to raw audio. For example, the
TIDIGITS test files are in SPHERE format, so it is necessary to convert them
to raw audio format before it can be read by the Sphinx-4 front end. This is
usually accomplished by using the program sox on UNIX
platforms.
- Create a batch file - a test database usually contains a
transcript (i.e., the actual text of the speech data) of all the test files.
Using the transcript file, create a batch file,
listing the location of the test files and their corresponding transcript.
For example, our
tests/performance/an4/an4_full.batch file
looks like: /lab/speech/sphinx4/data/an4/an4_clstk/fash/an251-fash-b.raw yes
/lab/speech/sphinx4/data/an4/an4_clstk/fash/an253-fash-b.raw go
/lab/speech/sphinx4/data/an4/an4_clstk/fash/an254-fash-b.raw yes
/lab/speech/sphinx4/data/an4/an4_clstk/fash/an255-fash-b.raw u m n y h six
...
All batch files should reside in the test directory, in this case
tests/performance/an4.
- Acoustic model & Dictionary - use the Wall Street Journal
(WSJ) models for the AN4 test, which is already included as a JAR file in
the binary distribution (sphinx4-{version}-bin.zip). If you downloaded the
source distribution, building it by running
ant at the top
level directory will create the JAR file for the WSJ model. The JAR file
should be included in the classpath of the application you are deploying. In
this case, the WSJ JAR file
(lib/WSJ_8gau_13dCep_16k_40mel_130Hz_6800Hz.jar) is included in
the java command line inside the build.xml run file. We also need to specify
in the config file (see the next item below) the acoustic model class we are
using, which in this case is
edu.cmu.sphinx.model.acoustic.WSJ_8gau_13dCep_16k_40mel_130Hz_6800Hz
. The dictionary is also specified in the config file using the
resource mechanism of Sphinx-4.
- Creating the config file & grammar files - In order to create
your own configuration file, you must first understand the Sphinx-4
configuration management system. The AN4 config file is
tests/performance/an4/an4.config.xml, please take a look at it.
This file describes how the batch-mode recognizer and its various
sub-components should be configured. Note that this file also contains
configurations for the live-mode recognizer, which is not the subject of
interest of this walkthrough. In the following we will refer to components
in the config file using highlights.
In an4.config.xml, the batch-mode recognizer is called
batch. It uses the Recognizer called
wordRecognizer, which contains the decoder, as
well as various monitors that keeps track of recognition accuracy, speed,
and memory. The decoder contains the
searchManager, which in turn contains the
linguist, the pruner, the scorer, and
the activeList. Refer to the Javadoc
(go to bottom of the page) for a description of each of these components.
The linguist used is the flatLinguist, and the grammar of the
flatLinguist is either the wordListGrammar, which
is a file with a list of words, e.g.,
AND
APOSTROPHE
APRIL
AREA
AUGUST
CODE
the lmGrammar (i.e., N-gram language model), or
fstGrammar (i.e., finite state tranducer grammar). The
lmGrammar uses a language model file (text-based for AN4)
generated by the CMU
Statistical Language Modeling (SLM) Toolkit. The
flatLinguist also specifies the acoustic model used, and in
this case it is the WSJ models. The location and format of the WSJ model, as
well as the location of the various files in the model, are also specified.
The scorer contains the front end, which is called
mfcFrontEnd since it produces MFCC features.
- Creating a build.xml for Ant - a file called
build.xml is necessary to run Ant. This file is the Ant version
of the Makefile in Make. All Ant targets are listed in this file. For
details on how to write this file, refer to the documentation at http://ant.apache.org/. Lets use the first
Ant target, an4_words_wordlist, as an example. This Ant target
invokes the java command on the class
edu.cmu.sphinx.tools.batch.BatchModeRecognizer. This class
takes a configuration file (an4.config.xml) and a batch file
(an4_words.batch) as arguments. This class looks for the
component named batch in the configuration file. The
configuration manager will create this component (and its subcomponents).
Therefore, the component
edu.cmu.sphinx.tools.batch.BatchModeRecognizer should always be
named "batch" in the config.xml file. Other AN4 Ant targets are
created similarly.
- Setup Complete - At this point, we have completed the setup of
the AN4 tests. You can now run the AN4 tests by following instructions in small
vocabulary tests.
- Acoustic Models
The two main acoustic models that are used by Sphinx-4, TIDIGITS and Wall
Street Journal, are already included in the "lib" directory of
the binary distribution. For the source distribution, you will build it when
you type ant at the top level directory. Our regression tests
also uses the RM1 and HUB4 models, which are available for download separately
on the download page. Sphinx-4 can handle model packages provided as a jar
file.
Each acoustic model implements the AcousticModel
interface. For example, the WSJ models are wrapped by a class called
edu.cmu.sphinx.model.acoustic.WSJ_8gau_13dCep_16k_40mel_130Hz_6800Hz,
which implements the AcousticModel interface. This implementation class is in
the JAR file of the models, together with the actual data files of the model.
This way, two simple steps are need to use a particular acoustic model:
- Include the JAR file in your classpath.
- Specify the model implementation class in the config file.
You can find out the model implementation class of a JAR file using the
java -jar command. For example, you can find out the model class
of the WSJ model by:
sphinx4>java -jar lib/WSJ_8gau_13dCep_16k_40mel_130Hz_6800Hz.jar
Wall Street Journal acoustic models
Class: edu.cmu.sphinx.model.acoustic.WSJ_8gau_13dCep_16k_40mel_130Hz_6800Hz
Is Binary: true
Sparse Form: false
Filters: 40
Vector Length: 39
Gaussians: 8
Model Definition: etc/WSJ_clean_13dCep_16k_40mel_130Hz_6800Hz.4000.mdef
Data Location: cd_continuous_8gau
Feature Type: cepstra_delta_doubledelta
Sample Rate: 16000
Description: Wall Street Journal acoustic models
Number Fft Points: 512
Max Freq: 6800
Min Freq.: 130
The print out also includes details about how the model was trained, but
this is not important for the average user.
- Language Models
The language model used by Sphinx-4 follows the ARPA format. Language
models provided with the acoustic model packages were created with the
Carnegie Mellon University Statistical Language Modeling toolkit (CMU SLM
toolkit), available at
CMU. A manual is available there.
The language model is created from a list of transcriptions. Given a file
with training transcription, the following script creates a list of words that
appear in the transcriptions, then creates a bigram and a trigram LM files in
the ARPA format. The file with extension ccs contains the context cues, and it
is usually a list of words used as markers - beginning or end of speech
etc.
set task = RM
# Location of the CMU SLM toolkit
set bindir = ~/src/CMU-SLM_Toolkit_v2/bin
cat $task.transcript | $bindir/text2wfreq | $bindir/wfreq2vocab > $task.vocab
set mode = "-absolute"
# Create bigram
cat $task.transcript | $bindir/text2idngram -n 2 -vocab $task.vocab | \
$bindir/idngram2lm $mode -context $task.ccs -n 2 -vocab $task.vocab \
-idngram - -arpa $task.bigram.arpa
# Create trigram
cat $task.transcript | $bindir/text2idngram -n 3 -vocab $task.vocab | \
$bindir/idngram2lm $mode -context $task.ccs -n 3 -vocab $task.vocab \
-idngram - -arpa $task.trigram.arpa
- BNF-Style Grammars
Sphinx-4 uses the Java Speech API Grammar Format (JSGF) to perform speech
recognition using a BNF-style grammar. Currently, you can only use JSGF
grammars with the FlatLinguist. To specify JSGF grammars, set the following in
the configuration file:
<component name="flatLinguist" type="edu.cmu.sphinx.linguist.flat.FlatLinguist">
<property name="grammar" value="jsgfGrammar">
// ... other properties ...
</component>
<component name="jsgfGrammar" type="edu.cmu.sphinx.jsapi.JSGFGrammar">
<property name="grammarLocation" value="...URL of grammar directory"/>
</component>
For information on how to write JSGF grammars, and how to specify the
location of your JSGF grammar file(s), and the limitations of the current
implementation of JSGF grammar, please refer to the Javadocs
for JSGFGrammar.
- Architecture and API
The Sphinx-4 API can be found in the javadoc
documentation.
If the previous is broken, please build the javadocs using the instructions
in Creating
Javadocs. In fact, rebuilding javadocs is something you should do every
time you change code in Sphinx-4.
In this section, we will provide an overview of Sphinx-4, starting with an
introduction of HMM-based recognizers. We will highlight in
red those keywords that are critical to understanding
Sphinx-4.
Overview of an HMM-based Speech Recognition System
Sphinx-4 is an HMM-based speech recognizer. HMM
stands for Hidden Markov Models, which is a type of statistical model. In
HMM-based speech recognizers, each unit of sound (usually called a phoneme) is
represented by a statistical model that represents the distribution of all the
evidence (data) for that phoneme. This is called the acoustic
model for that phoneme. When creating an acoustic model, the
speech signals are first transformed into a sequence of vectors that represent
certain characteristics of the signal, and the parameters of the acoustic
model are then estimated using these vectors (usually called
features). This process is called training the acoustic
models.
During speech recognition, features are derived from the incoming speech
(we will use "speech" to mean the same thing as "audio") in the same way as in
the training process. The component of the recognizer that generates these
features is called the front end. These live features
are scored against the acoustic model. The score
obtained indicates how likely that a particular set of features (extracted
from live audio) belongs to the phoneme of the corresponding acoustic model.
The process of speech recognition is to find the best possible sequence of
words (or units) that will fit the given input speech. It is a
search problem, and in the case of HMM-based
recognizers, a graph search problem. The graph represents all possible
sequences of phonemes in the entire language of the
task under consideration. The graph is typically composed of the HMMs of sound
units concatenated in a guided manner, as specified by the
grammar of the task. As an example, lets look at a
simple search graph that decodes the words "one" and "two". It is composed of
the HMMs of the sounds units of the words "one" and "two":
Constructing the above graph requires knowledge from various sources. It
requires a dictionary, which maps the word "one" to the
phonemes W, AX and N, and the word "two" to T and OO. It requires the acoustic
model to obtain the HMMs for the phonemes W, AX, N, T and OO. In Sphinx-4, the
task of constructing this search graph is done by the
linguist.
Usually, the search graph also has information about how likely certain
words will occur. This information is supplied by the language
model. Suppose that, in our example, the probability of someone
saying "one" (e.g., 0.8) is much higher than saying "two" (0.2). Then, in the
above graph, the probability of the transition between the entry node and the
first node of the HMM for W will be 0.8, while the probability of the
transition between the entry node and the first node of the HMM for T will be
0.2. The path to "one" will consequently have a higher score.
Once this graph is constructed, the sequence of parametrized speech signals
(i.e., the features) is matched against different paths through the graph to
find the best fit. The best fit is usually the least cost or highest scoring
path, depending on the implementation. In Sphinx-4, the task of searching
through the graph for the best path is done by the search
manager.
As you can see from the above graph, a lot of the nodes have self
transitions. This can lead to a very large number of possible paths through
the graph. As a result, finding the best possible path can take a very long
time. The purpose of the pruner is to reduce the number
of possible paths during the search, using heuristics like pruning away the
lowest scoring paths.
As we described earlier, the input speech signal is transformed into a
sequence of feature vectors. After the last feature vector is decoded, we look
at all the paths that have reached the final exit node (the red node). The
path with the highest score is the best fit, and a
result taking all the words of that path is returned.
In this section, we describe the main components of Sphinx-4, and how they
work together during the recognition process. First of all, lets look at the
architecture diagram of Sphinx-4. It contains almost all the concepts (the
words in red) that were introduced in the previous section. There are a few
additional concepts in the diagram, which we will explain promptly.
When the recognizer starts up, it constructs the front end (which generates
features from speech), the decoder, and the linguist (which generates the
search graph) according to the configuration specified by the user. These
components will in turn construct their own subcomponents. For example, the
linguist will construct the acoustic model, the dictionary, and the language
model. It will use the knowledge from these three components to construct a
search graph that is appropriate for the task. The decoder will construct the
search manager, which in turn constructs the scorer, the pruner, and the
active list.
Most of these components represents interfaces. The search manager,
linguist, acoustic model, dictionary, language model, active list, scorer,
pruner, and search graph are all Java interfaces. There can be different
implementations of these interfaces. For example, there are two different
implementations of the search manager. Then, how does the system know which
implementation to use? It is specified by the user via the configuration file,
an XML-based file that is loaded by the configuration
manager. In this configuration file, the user can also specify the
properties of the implementations. One example of a
property is the sample rate of the incoming speech data.
The active list is a component that requires
explanation. Remember we mentioned that there can be many possible paths
through the search graph. Sphinx-4 currently implements a
token-passing algorithm. Each time the search arrives
at the next state in the graph, a token is created. A token points to the
previous token, as well as the next state. The active list keeps track of all
the current active paths through the search graph by storing the last token of
each path. A token has the score of the path at that particular point in the
search. To perform pruning, we simply prune the tokens in the active list.
When the application asks the recognizer to perform recognition, the search
manager will ask the scorer to score each token in the active list against the
next feature vector obtained from the front end. This gives a new score for
each of the active paths. The pruner will then prune the tokens (i.e., active
paths) using certain heuristics. Each surviving paths will then be expanded to
the next states, where a new token will be created for each next state. The
process repeats itself until no more feature vectors can be obtained from the
front end for scoring. This usually means that there is no more input speech
data. At that point, we look at all paths that have reached the final exit
state, and return the highest scoring path as the result to the application.
The performance of Sphinx-4 critically depends on your task and how you
configured Sphinx-4 to suit your task. For example, a large vocabulary task
needs a different linguist than a small vocabulary task. Your system has to be
configured differently for the two tasks. This section will not tell you the
exact configuration for different tasks, which will be dealt with later.
Instead, this section will introduce you to the configuration mechanism of
Sphinx-4, which is via an XML-based configuration file. Please click on the
document Sphinx-4
Configuration Management to learn how to do this. It is important that you
read this document before you proceed.
