 |
|
| |
julius
julius
- open source multi-purpose LVCSR engine
julius [-C jconffile]
[options...]
julius is a high-performance, multi-purpose, open-source speech
recognition engine for researchers and developers. It is capable of performing
almost real-time recognition of continuous speech with over 60k-word 3-gram
language model and triphone HMM model, on most current PCs. julius can
perform recognition on audio files, live microphone input, network input and
feature parameter files.
The core recognition module is implemented as C library called
"JuliusLib". It can also be extended by plug-in facility.
julius needs a language model and an acoustic model to run as a speech
recognizer. julius supports the following models.
Acoustic model
Sub-word HMM (Hidden Markov Model) in HTK ascii format are
supported. Phoneme models (monophone), context dependent phoneme models
(triphone), tied-mixture and phonetic tied-mixture models of any unit can be
used. When using context dependent models, inter-word context dependency is
also handled. Multi-stream feature and MSD-HMM is also supported. You can
further use a tool mkbinhmm to convert the ascii HMM file to a
compact binary format for faster loading.
Note that julius itself can only extract MFCC features from
speech data. If you use acoustic HMM trained for other feature, you should
give the input in HTK parameter file of the same feature type.
Language model: word N-gram
Word N-gram language model, up to 10-gram, is supported. Julius
uses different N-gram for each pass: left-to-right 2-gram on 1st pass, and
right-to-left N-gram on 2nd pass. It is recommended to use both LR 2-gram
and RL N-gram for Julius. However, you can use only single LR N-gram or RL
N-gram. In such case, approximated LR 2-gram computed from the given N-gram
will be applied at the first pass.
The Standard ARPA format is supported. In addition, a binary
format is also supported for efficiency. The tool mkbingram(1) can
convert ARPA format N-gram to binary format.
Language model: grammar
The grammar format is an original one, and tools to create a
recognirion grammar are included in the distribution. A grammar consists of
two files: one is a 'grammar' file that describes sentence structures in a
BNF style, using word 'category' name as terminate symbols. Another is a
'voca' file that defines words with its pronunciations (i.e. phoneme
sequences) for each category. They should be converted by mkdfa.pl(1)
to a deterministic finite automaton file (.dfa) and a dictionary file
(.dict), respectively. You can also use multiple grammars.
Language model: isolated word
You can perform isolated word recognition using only word
dictionary. With this model type, Julius will perform rapid one pass
recognition with static context handling. Silence models will be added at
both head and tail of each word. You can also use multiple dictionaries in a
process.
Recognition algorithm of julius is based on a two-pass strategy. Word
2-gram and reverse word 3-gram is used on the respective passes. The entire
input is processed on the first pass, and again the final searching process is
performed again for the input, using the result of the first pass to narrow
the search space. Specifically, the recognition algorithm is based on a
tree-trellis heuristic search combined with left-to-right frame-synchronous
beam search and right-to-left stack decoding search.
When using context dependent phones (triphones), interword
contexts are taken into consideration. For tied-mixture and phonetic
tied-mixture models, high-speed acoustic likelihood calculation is possible
using gaussian pruning.
For more details, see the related documents.
These options specify the models, system behaviors and various search parameters
to Julius. These option can be set at the command line, but it is recommended
that you write them in a text file as a "jconf file", and specify it
by "-C" option.
Applications incorporating JuliusLib also use these options to set
the parameters of core recognition engine. For example, a jconf file can be
loaded to the enine by calling j_config_load_file_new() with the
jconf file name as argument.
Please note that relative paths in a jconf file should be relative
to the jconf file itself, not the current working directory.
Below are the details of all options, gathered by group.
These are application options of Julius, outside of JuliusLib. It contains
parameters and switches for result output, character set conversion, log
level, and module mode options. These option are specific to Julius, and
cannot be used at applications using JuliusLib other than Julius.
-outfile
On file input, this option write the recognition result
of each file to a separate file. The output file of an input file will be the
same name but the suffix will be changed to ".out". (rev.4.0)
-separatescore
Output the language and acoustic scores separately.
-callbackdebug
Print the callback names at each call for debug.
(rev.4.0)
-charconv from to
Print with character set conversion. from is the
source character set used in the language model, and to is the target
character set you want to get.
On Linux, the arguments should be a code name. You can obtain the
list of available code names by invoking the command "iconv
--list". On Windows, the arguments should be a code name or codepage
number. Code name should be one of "ansi", "mac",
"oem", "utf-7", "utf-8", "sjis",
"euc". Or you can specify any codepage number supported at your
environment.
-nocharconv
Disable character conversion.
-module [port]
Run Julius on "Server Module Mode". After
startup, Julius waits for tcp/ip connection from client. Once connection is
established, Julius start communication with the client to process incoming
commands from the client, or to output recognition results, input trigger
information and other system status to the client. The default port number is
10500.
-record dir
Auto-save all input speech data into the specified
directory. Each segmented inputs are recorded each by one. The file name of
the recorded data is generated from system time when the input ends, in a
style of YYYY.MMDD.HHMMSS.wav. File format is 16bit monoral WAV. Invalid for
mfcfile input.
With input rejection by -rejectshort, the rejected input
will also be recorded even if they are rejected.
-logfile file
Save all log output to a file instead of standard output.
(Rev.4.0)
-nolog
Disable all log output. (Rev.4.0)
-help
Output help message and exit.
These are model-/search-dependent options relating audio input, sound detection,
GMM, decoding algorithm, plugin facility, and others. Global options should be
placed before any instance declaration (-AM, -LM, or
-SR), or just after "-GLOBAL" option.
Audio input
-input
{mic|rawfile|mfcfile|adinnet|stdin|netaudio|alsa|oss|esd}
Choose speech input source. Specify 'file' or 'rawfile'
for waveform file, 'htkparam' or 'mfcfile' for HTK parameter file. On file
input, users will be prompted to enter the file name from stdin, or you can
use -filelist option to specify list of files to process.
´mic' is to get audio input from a default live microphone
device, and 'adinnet' means receiving waveform data via tcpip network from
an adinnet client. 'netaudio' is from DatLink/NetAudio input, and 'stdin'
means data input from standard input.
For waveform file input, only WAV (no compression) and RAW
(noheader, 16bit, big endian) are supported by default. Other format can be
read when compiled with libsnd library. To see what format is actually
supported, see the help message using option -help. For stdin input,
only WAV and RAW is supported. (default: mfcfile)
At Linux, you can choose API at run time by specifying alsa, oss
and esd.
-filelist filename
(With -input rawfile|mfcfile) perform recognition
on all files listed in the file. The file should contain input file per line.
Engine will end when all of the files are processed.
-notypecheck
By default, Julius checks the input parameter type
whether it matches the AM or not. This option will disable the check and force
engine to use the input vector as is.
-48
Record input with 48kHz sampling, and down-sample it to
16kHz on-the-fly. This option is valid for 16kHz model only. The down-sampling
routine was ported from sptk. (Rev. 4.0)
-NA devicename
Host name for DatLink server input (-input
netaudio).
-adport port_number
With -input adinnet, specify adinnet port number
to listen. (default: 5530)
-nostrip
Julius by default removes successive zero samples in
input speech data. This option inhibits the removal.
-zmean , -nozmean
This option enables/disables DC offset removal of input
waveform. Offset will be estimated from the whole input. For microphone /
network input, zero mean of the first 48000 samples (3 seconds in 16kHz
sampling) will be used for the estimation. (default: disabled)
This option uses static offset for the channel. See also
-zmeansource for frame-wise offset removal.
Speech detection by level and zero-cross
-cutsilence , -nocutsilence
Turn on / off the speech detection by level and
zero-cross. Default is on for mic / adinnet input, and off for files.
-lv thres
Level threshold for speech input detection. Values should
be in range from 0 to 32767. (default: 2000)
-zc thres
Zero crossing threshold per second. Only input that goes
over the level threshold (-lv) will be counted. (default: 60)
-headmargin msec
Silence margin at the start of speech segment in
milliseconds. (default: 300)
-tailmargin msec
Silence margin at the end of speech segment in
milliseconds. (default: 400)
Input rejection
Two simple front-end input rejection methods are implemented,
based on input length and average power of detected segment. The rejection
by average power is experimental, and can be enabled by
--enable-power-reject on compilation. Valid for MFCC feature with power
coefficient and real-time input only.
For GMM-based input rejection see the GMM section below.
-rejectshort msec
Reject input shorter than specified milliseconds. Search
will be terminated and no result will be output.
-powerthres thres
Reject the inputted segment by its average energy. If the
average energy of the last recognized input is below the threshold, Julius
will reject the input. (Rev.4.0)
This option is valid when --enable-power-reject is specified at
compilation time.
Gaussian mixture model / GMM-VAD
GMM will be used for input rejection by accumulated score, or for
front-end GMM-based VAD when --enable-gmm-vad is specified.
NOTE: You should also set the proper MFCC parameters required for
the GMM, specifying the acoustic parameters described in AM section
-AM_GMM.
When GMM-based VAD is enabled, the voice activity score will be
calculated at each frame as front-end processing. The value will be computed
as \[ \max_{m \in M_v} p(x|m) - \max_{m \in M_n} p(x|m) \] where $M_v$ is a
set of voice GMM, and $M_n$ is a set of noise GMM whose names should be
specified by -gmmreject. The activity score will be then averaged for
the last N frames, where N is specified by -gmmmargin. Julius updates
the averaged activity score at each frame, and detect speech up-trigger when
the value gets higher than a value specified by -gmmup, and detecgt
down-trigger when it gets lower than a value of -gmmdown.
-gmm hmmdefs_file
GMM definition file in HTK format. If specified,
GMM-based input verification will be performed concurrently with the 1st pass,
and you can reject the input according to the result as specified by
-gmmreject. The GMM should be defined as one-state HMMs.
-gmmnum number
Number of Gaussian components to be computed per frame on
GMM calculation. Only the N-best Gaussians will be computed for rapid
calculation. The default is 10 and specifying smaller value will speed up GMM
calculation, but too small value (1 or 2) may cause degradation of
identification performance.
-gmmreject string
Comma-separated list of GMM names to be rejected as
invalid input. When recognition, the log likelihoods of GMMs accumulated for
the entire input will be computed concurrently with the 1st pass. If the GMM
name of the maximum score is within this string, the 2nd pass will not be
executed and the input will be rejected.
-gmmmargin frames
(GMM_VAD) Head margin in frames. When a speech trigger
detected by GMM, recognition will start from current frame minus this value.
(Rev.4.0)
This option will be valid only if compiled with
--enable-gmm-vad.
-gmmup value
(GMM_VAD) Up trigger threshold of voice activity score.
(Rev.4.1)
This option will be valid only if compiled with
--enable-gmm-vad.
-gmmdown value
(GMM_VAD) Down trigger threshold of voice activity score.
(Rev.4.1)
This option will be valid only if compiled with
--enable-gmm-vad.
Decoding option
Real-time processing means concurrent processing of MFCC
computation 1st pass decoding. By default, real-time processing on the pass
is on for microphone / adinnet / netaudio input, and for others.
-realtime , -norealtime
Explicitly switch on / off real-time (pipe-line)
processing on the first pass. The default is off for file input, and on for
microphone, adinnet and NetAudio input. This option relates to the way CMN and
energy normalization is performed: if off, they will be done using average
features of whole input. If on, MAP-CMN and energy normalization to do
real-time processing.
Misc. options
-C jconffile
Load a jconf file at here. The content of the jconffile
will be expanded at this point.
-version
Print version information to standard error, and
exit.
-setting
Print engine setting information to standard error, and
exit.
-quiet
Output less log. For result, only the best word sequence
will be printed.
-debug
(For debug) output enormous internal message and debug
information to log.
-check {wchmm|trellis|triphone}
For debug, enter interactive check mode.
-plugindir dirlist
Specify directory to load plugin. If several direcotries
exist, specify them by colon-separated list.
The following arguments will create a new configuration set with default
parameters, and switch current set to it. Jconf parameters specified after the
option will be set into the current set.
To do multi-model decoding, these argument should be specified at
the first of each model / search instances with different names. Any options
before the first instance definition will be IGNORED.
When no instance definition is found (as older version of Julius),
all the options are assigned to a default instance named _default.
Please note that decoding with a single LM and multiple AMs is not
fully supported. For example, you may want to construct the jconf file as
following.
This type of model sharing is not supported yet, since some part
of LM processing depends on the assigned AM. Instead, you can get the same
result by defining the same LMs for each AM, like this:
-AM name
Create a new AM configuration set, and switch current to
the new one. You should give a unique name. (Rev.4.0)
-LM name
Create a new LM configuration set, and switch current to
the new one. You should give a unique name. (Rev.4.0)
-SR name am_name lm_name
Create a new search configuration set, and switch current
to the new one. The specified AM and LM will be assigned to it. The
am_name and lm_name can be either name or ID number. You should
give a unique name. (Rev.4.0)
-AM_GMM
When using GMM for front-end processing, you can specify
GMM-specific acoustic parameters after this option. If you does not specify
-AM_GMM with GMM, the GMM will share the same parameter vector as the
last AM. The current AM will be switched to the GMM one, so be careful not to
confuse with normal AM configurations. (Rev.4.0)
-GLOBAL
Start a global section. The global options should be
placed before any instance declaration, or after this option on multiple model
recognition. This can be used multiple times. (Rev.4.1)
-nosectioncheck , -sectioncheck
Disable / enable option location check in multi-model
decoding. When enabled, the options between instance declaration is treated as
"sections" and only the belonging option types can be written. For
example, when an option -AM is specified, only the AM related option
can be placed after the option until other declaration is found. Also, global
options should be placed at top, before any instance declarataion. This is
enabled by default. (Rev.4.1)
This group contains options for model definition of each language model type.
When using multiple LM, one instance can have only one LM.
Only one type of LM can be specified for a LM configuration. If
you want to use multi model, you should define them one as a new LM.
N-gram
-d bingram_file
Use binary format N-gram. An ARPA N-gram file can be
converted to Julius binary format by mkbingram.
-nlr arpa_ngram_file
A forward, left-to-right N-gram language model in
standard ARPA format. When both a forward N-gram and backward N-gram are
specified, Julius uses this forward 2-gram for the 1st pass, and the backward
N-gram for the 2nd pass.
Since ARPA file often gets huge and requires a lot of time to
load, it may be better to convert the ARPA file to Julius binary format by
mkbingram. Note that if both forward and backward N-gram is used for
recognition, they together will be converted to a single binary.
When only a forward N-gram is specified by this option and no
backward N-gram specified by -nrl, Julius performs recognition with
only the forward N-gram. The 1st pass will use the 2-gram entry in the given
N-gram, and The 2nd pass will use the given N-gram, with converting forward
probabilities to backward probabilities by Bayes rule. (Rev.4.0)
-nrl arpa_ngram_file
A backward, right-to-left N-gram language model in
standard ARPA format. When both a forward N-gram and backward N-gram are
specified, Julius uses the forward 2-gram for the 1st pass, and this backward
N-gram for the 2nd pass.
Since ARPA file often gets huge and requires a lot of time to
load, it may be better to convert the ARPA file to Julius binary format by
mkbingram. Note that if both forward and backward N-gram is used for
recognition, they together will be converted to a single binary.
When only a backward N-gram is specified by this option and no
forward N-gram specified by -nlr, Julius performs recognition with
only the backward N-gram. The 1st pass will use the forward 2-gram
probability computed from the backward 2-gram using Bayes rule. The 2nd pass
fully use the given backward N-gram. (Rev.4.0)
-v dict_file
Word dictionary file.
-silhead word_string -siltail
word_string
Silence word defined in the dictionary, for silences at
the beginning of sentence and end of sentence. (default:
"<s>", "</s>")
-mapunk word_string
Specify unknown word. Default is "<unk>"
or "<UNK>". This will be used to assign word probability on
unknown words, i.e. words in dictionary that are not in N-gram
vocabulary.
-iwspword
Add a word entry to the dictionary that should correspond
to inter-word pauses. This may improve recognition accuracy in some language
model that has no explicit inter-word pause modeling. The word entry to be
added can be changed by -iwspentry.
-iwspentry word_entry_string
Specify the word entry that will be added by
-iwspword. (default: "<UNK> [sp] sp sp")
-sepnum number
Number of high frequency words to be isolated from the
lexicon tree, to ease approximation error that may be caused by the one-best
approximation on 1st pass. (default: 150)
Grammar
Multiple grammars can be specified by repeating -gram and
-gramlist. Note that this is unusual behavior from other options (in
normal Julius option, last one will override previous ones). You can use
-nogram to reset the grammars already specified before the point.
-gram gramprefix1[,gramprefix2[,gramprefix3,...]]
Comma-separated list of grammars to be used. the argument
should be a prefix of a grammar, i.e. if you have foo.dfa and
foo.dict, you should specify them with a single argument foo. Multiple
grammars can be specified at a time as a comma-separated list.
-gramlist list_file
Specify a grammar list file that contains list of
grammars to be used. The list file should contain the prefixes of grammars,
each per line. A relative path in the list file will be treated as relative to
the file, not the current path or configuration file.
-dfa dfa_file -v dict_file
An old way of specifying grammar files separately. This
is bogus, and should not be used any more.
-nogram
Remove the current list of grammars already specified by
-gram, -gramlist, -dfa and -v.
Isolated word
Dictionary can be specified by using -w and -wlist.
When you specify multiple times, all of them will be read at startup. You
can use -nogram to reset the already specified dictionaries at that
point.
-w dict_file
Word dictionary for isolated word recognition. File
format is the same as other LM. (Rev.4.0)
-wlist list_file
Specify a dictionary list file that contains list of
dictionaries to be used. The list file should contain the file name of
dictionaries, each per line. A relative path in the list file will be treated
as relative to the list file, not the current path or configuration file.
(Rev.4.0)
-nogram
Remove the current list of dictionaries already specified
by -w and -wlist.
-wsil head_sil_model_name
tail_sil_model_name sil_context_name
On isolated word recognition, silence models will be
appended to the head and tail of each word at recognition. This option
specifies the silence models to be appended. sil_context_name is the
name of the head sil model and tail sil model as a context of word head phone
and tail phone. For example, if you specify -wsil silB silE sp, a word with
phone sequence b eh t will be translated as silB sp-b+eh b-eh+t eh-t+sp silE.
(Rev.4.0)
User-defined LM
-userlm
Declare to use user LM functions in the program. This
option should be specified if you use user-defined LM functions.
(Rev.4.0)
Misc. LM options
-forcedict
Skip error words in dictionary and force running.
This section is about options for acoustic model, feature extraction, feature
normalizations and spectral subtraction.
After -AM name, an acoustic model and related specification should
be written. You can use multiple AMs trained with different MFCC types. For
GMM, the required parameter condition should be specified just as same as
AMs after -AM_GMM.
When using multiple AMs, the values of -smpPeriod,
-smpFreq, -fsize and -fshift should be the same among
all AMs.
Acoustic HMM
-h hmmdef_file
Acoustic HMM definition file. It should be in HTK ascii
format, or Julius binary format. You can convert HTK ascii format to Julius
binary format using mkbinhmm.
-hlist hmmlist_file
HMMList file for phone mapping. This file provides
mapping between logical triphone names generated in the dictionary and the
defined HMM names in hmmdefs. This option should be specified for
context-dependent model.
-tmix number
Specify the number of top Gaussians to be calculated in a
mixture codebook. Small number will speed up the acoustic computation, but AM
accuracy may get worse with too small value. See also -gprune.
(default: 2)
-spmodel name
Specify HMM model name that corresponds to short-pause in
an utterance. The short-pause model name will be used in recognition:
short-pause skipping on grammar recognition, word-end short-pause model
insertion with -iwsp on N-gram, or short-pause segmentation
(-spsegment). (default: "sp")
-multipath
Enable multi-path mode. To make decoding faster, Julius
by default impose a limit on HMM transitions that each model should have only
one transition from initial state and to end state. On multi-path mode, Julius
does extra handling on inter-model transition to allows model-skipping
transition and multiple output/input transitions. Note that specifying this
option will make Julius a bit slower, and the larger beam width may be
required.
This function was a compilation-time option on Julius 3.x, and now
becomes a run-time option. By default (without this option), Julius checks
the transition type of specified HMMs, and enable the multi-path mode if
required. You can force multi-path mode with this option. (rev.4.0)
-gprune {safe|heuristic|beam|none|default}
Set Gaussian pruning algorithm to use. For tied-mixture
model, Julius performs Gaussian pruning to reduce acoustic computation, by
calculating only the top N Gaussians in each codebook at each frame. The
default setting will be set according to the model type and engine setting.
default will force accepting the default setting. Set this to none to disable
pruning and perform full computation. safe guarantees the top N Gaussians to
be computed. heuristic and beam do more aggressive computational cost
reduction, but may result in small loss of accuracy model (default: safe
(standard), beam (fast) for tied mixture model, none for non tied-mixture
model).
-iwcd1 {max|avg|best number}
Select method to approximate inter-word triphone on the
head and tail of a word in the first pass.
max will apply the maximum likelihood of the same context
triphones. avg will apply the average likelihood of the same context
triphones. best number will apply the average of top N-best likelihoods of
the same context triphone.
Default is best 3 for use with N-gram, and avg for grammar and
word. When this AM is shared by LMs of both type, latter one will be
chosen.
-iwsppenalty float
Insertion penalty for word-end short pauses appended by
-iwsp.
-gshmm hmmdef_file
If this option is specified, Julius performs Gaussian
Mixture Selection for efficient decoding. The hmmdefs should be a monophone
model generated from an ordinary monophone HMM model, using mkgshmm.
-gsnum number
On GMS, specify number of monophone states to compute
corresponding triphones in detail. (default: 24)
Speech analysis
Only MFCC feature extraction is supported in current Julius. Thus
when recognizing a waveform input from file or microphone, AM must be
trained by MFCC. The parameter condition should also be set as exactly the
same as the training condition by the options below.
When you give an input in HTK Parameter file, you can use any
parameter type for AM. In this case Julius does not care about the type of
input feature and AM, just read them as vector sequence and match them to
the given AM. Julius only checks whether the parameter types are the same.
If it does not work well, you can disable this checking by
-notypecheck.
In Julius, the parameter kind and qualifiers (as TARGETKIND in
HTK) and the number of cepstral parameters (NUMCEPS) will be set
automatically from the content of the AM header, so you need not specify
them by options.
Other parameters should be set exactly the same as training
condition. You can also give a HTK Config file which you used to train AM to
Julius by -htkconf. When this option is applied, Julius will parse
the Config file and set appropriate parameter.
You can further embed those analysis parameter settings to a
binary HMM file using mkbinhmm.
If options specified in several ways, they will be evaluated in
the order below. The AM embedded parameter will be loaded first if any.
Then, the HTK config file given by -htkconf will be parsed. If a
value already set by AM embedded value, HTK config will override them. At
last, the direct options will be loaded, which will override settings loaded
before. Note that, when the same options are specified several times, later
will override previous, except that -htkconf will be evaluated first
as described above.
-smpPeriod period
Sampling period of input speech, in unit of 100
nanoseconds. Sampling rate can also be specified by -smpFreq. Please
note that the input frequency should be set equal to the training conditions
of AM. (default: 625, corresponds to 16,000Hz)
This option corresponds to the HTK Option SOURCERATE. The same
value can be given to this option.
When using multiple AM, this value should be the same among all
AMs.
-smpFreq Hz
Set sampling frequency of input speech in Hz. Sampling
rate can also be specified using -smpPeriod. Please note that this
frequency should be set equal to the training conditions of AM. (default:
16,000)
When using multiple AM, this value should be the same among all
AMs.
-fsize sample_num
Window size in number of samples. (default: 400)
This option corresponds to the HTK Option WINDOWSIZE, but value
should be in samples (HTK value / smpPeriod).
When using multiple AM, this value should be the same among all
AMs.
-fshift sample_num
Frame shift in number of samples. (default: 160)
This option corresponds to the HTK Option TARGETRATE, but value
should be in samples (HTK value / smpPeriod).
When using multiple AM, this value should be the same among all
AMs.
-preemph float
Pre-emphasis coefficient. (default: 0.97)
This option corresponds to the HTK Option PREEMCOEF. The same
value can be given to this option.
-fbank num
Number of filterbank channels. (default: 24)
This option corresponds to the HTK Option NUMCHANS. The same value
can be given to this option. Be aware that the default value not the same as
in HTK (22).
-ceplif num
Cepstral liftering coefficient. (default: 22)
This option corresponds to the HTK Option CEPLIFTER. The same
value can be given to this option.
-rawe , -norawe
Enable/disable using raw energy before pre-emphasis
(default: disabled)
This option corresponds to the HTK Option RAWENERGY. Be aware that
the default value differs from HTK (enabled at HTK, disabled at Julius).
-enormal , -noenormal
Enable/disable normalizing log energy. On live input,
this normalization will be approximated from the average of last input.
(default: disabled)
This option corresponds to the HTK Option ENORMALISE. Be aware
that the default value differs from HTK (enabled at HTK, disabled at
Julius).
-escale float_scale
Scaling factor of log energy when normalizing log energy.
(default: 1.0)
This option corresponds to the HTK Option ESCALE. Be aware that
the default value differs from HTK (0.1).
-silfloor float
Energy silence floor in dB when normalizing log energy.
(default: 50.0)
This option corresponds to the HTK Option SILFLOOR.
-delwin frame
Delta window size in number of frames. (default: 2)
This option corresponds to the HTK Option DELTAWINDOW. The same
value can be given to this option.
-accwin frame
Acceleration window size in number of frames. (default:
2)
This option corresponds to the HTK Option ACCWINDOW. The same
value can be given to this option.
-hifreq Hz
Enable band-limiting for MFCC filterbank computation: set
upper frequency cut-off. Value of -1 will disable it. (default: -1)
This option corresponds to the HTK Option HIFREQ. The same value
can be given to this option.
-lofreq Hz
Enable band-limiting for MFCC filterbank computation: set
lower frequency cut-off. Value of -1 will disable it. (default: -1)
This option corresponds to the HTK Option LOFREQ. The same value
can be given to this option.
-zmeanframe , -nozmeanframe
With speech input, this option enables/disables
frame-wise DC offset removal. This corresponds to HTK configuration
ZMEANSOURCE. This cannot be used together with -zmean. (default:
disabled)
-usepower
Use power instead of magnitude on filterbank analysis.
(default: disabled)
Normalization
Julius can perform cepstral mean normalization (CMN) for inputs.
CMN will be activated when the given AM was trained with CMN (i.e. has
"_Z" qualifier in the header).
The cepstral mean will be estimated in different way according to
the input type. On file input, the mean will be computed from the whole
input. On live input such as microphone and network input, the ceptral mean
of the input is unknown at the start. So MAP-CMN will be used. On MAP-CMN,
an initial mean vector will be applied at the beginning, and the mean vector
will be smeared to the mean of the incrementing input vector as input goes.
Options below can control the behavior of MAP-CMN.
-cvn
Enable cepstral variance normalization. At file input,
the variance of whole input will be calculated and then applied. At live
microphone input, variance of the last input will be applied. CVN is only
supported for an audio input.
-vtln alpha lowcut hicut
Do frequency warping, typically for a vocal tract length
normalization (VTLN). Arguments are warping factor, high frequency cut-off and
low freq. cut-off. They correspond to HTK Config values, WARPFREQ, WARPHCUTOFF
and WARPLCUTOFF.
-cmnload file
Load initial cepstral mean vector from file on startup.
The file should be one saved by -cmnsave. Loading an initial
cepstral mean enables Julius to better recognize the first utterance on a
real-time input. When used together with -cmnnoupdate, this initial
value will be used for all input.
-cmnsave file
Save the calculated cepstral mean vector into
file. The parameters will be saved at each input end. If the output
file already exists, it will be overridden.
-cmnupdate -cmnnoupdate
Control whether to update the cepstral mean at each input
on real-time input. Disabling this and specifying -cmnload will make
engine to always use the loaded static initial cepstral mean.
-cmnmapweight float
Specify the weight of initial cepstral mean for MAP-CMN.
Specify larger value to retain the initial cepstral mean for a longer period,
and smaller value to make the cepstral mean rely more on the current input.
(default: 100.0)
Front-end processing
Julius can perform spectral subtraction to reduce some stationary
noise from audio input. Though it is not a powerful method, but it may work
on some situation. Julius has two ways to estimate noise spectrum. One way
is to assume that the first short segment of an speech input is noise
segment, and estimate the noise spectrum as the average of the segment.
Another way is to calculate average spectrum from noise-only input using
other tool mkss, and load it in Julius. The former one is popular for speech
file input, and latter should be used in live input. The options below will
switch / control the behavior.
-sscalc
Perform spectral subtraction using head part of each file
as silence part. The head part length should be specified by
-sscalclen. Valid only for file input. Conflict with
-ssload.
-sscalclen msec
With -sscalc, specify the length of head silence
for noise spectrum estimation in milliseconds. (default: 300)
-ssload file
Perform spectral subtraction for speech input using
pre-estimated noise spectrum loaded from file. The noise spectrum file
can be made by mkss. Valid for all speech input. Conflict with
-sscalc.
-ssalpha float
Alpha coefficient of spectral subtraction for
-sscalc and -ssload. Noise will be subtracted stronger as this
value gets larger, but distortion of the resulting signal also becomes
remarkable. (default: 2.0)
-ssfloor float
Flooring coefficient of spectral subtraction. The
spectral power that goes below zero after subtraction will be substituted by
the source signal with this coefficient multiplied. (default: 0.5)
Misc. AM options
-htkconf file
Parse the given HTK Config file, and set corresponding
parameters to Julius. When using this option, the default parameter values are
switched from Julius defaults to HTK defaults.
This section contains options for search parameters on the 1st / 2nd pass such
as beam width and LM weights, configurations for short-pause segmentation,
switches for word lattice output and confusion network output, forced
alignments, and other options relating recognition process and result output.
Default values for beam width and LM weights will change according
to compile-time setup of JuliusLib , AM model type, and LM size. Please see
the startup log for the actual values.
1st pass parameters
-lmp weight penalty
(N-gram) Language model weights and word insertion
penalties for the first pass.
-penalty1 penalty
(Grammar) word insertion penalty for the first pass.
(default: 0.0)
-b width
Beam width in number of HMM nodes for rank beaming on the
first pass. This value defines search width on the 1st pass, and has dominant
effect on the total processing time. Smaller width will speed up the decoding,
but too small value will result in a substantial increase of recognition
errors due to search failure. Larger value will make the search stable and
will lead to failure-free search, but processing time will grow in proportion
to the width.
The default value is dependent on acoustic model type: 400
(monophone), 800 (triphone), or 1000 (triphone, setup=v2.1)
-nlimit num
Upper limit of token per node. This option is valid when
--enable-wpair and --enable-wpair-nlimit are enabled at compilation
time.
-progout
Enable progressive output of the partial results on the
first pass.
-proginterval msec
Set the time interval for -progout in
milliseconds. (default: 300)
2nd pass parameters
-lmp2 weight penalty
(N-gram) Language model weights and word insertion
penalties for the second pass.
-penalty2 penalty
(Grammar) word insertion penalty for the second pass.
(default: 0.0)
-b2 width
Envelope beam width (number of hypothesis) at the second
pass. If the count of word expansion at a certain hypothesis length reaches
this limit while search, shorter hypotheses are not expanded further. This
prevents search to fall in breadth-first-like situation stacking on the same
position, and improve search failure mostly for large vocabulary condition.
(default: 30)
-sb float
Score envelope width for enveloped scoring. When
calculating hypothesis score for each generated hypothesis, its trellis
expansion and Viterbi operation will be pruned in the middle of the speech if
score on a frame goes under the width. Giving small value makes the second
pass faster, but computation error may occur. (default: 80.0)
-s num
Stack size, i.e. the maximum number of hypothesis that
can be stored on the stack during the search. A larger value may give more
stable results, but increases the amount of memory required. (default:
500)
-m count
Number of expanded hypotheses required to discontinue the
search. If the number of expanded hypotheses is greater then this threshold
then, the search is discontinued at that point. The larger this value is, The
longer Julius gets to give up search. (default: 2000)
-n num
The number of candidates Julius tries to find. The search
continues till this number of sentence hypotheses have been found. The
obtained sentence hypotheses are sorted by score, and final result is
displayed in the order (see also the -output). The possibility that the
optimum hypothesis is correctly found increases as this value gets increased,
but the processing time also becomes longer. The default value depends on the
engine setup on compilation time: 10 (standard) or 1 (fast or v2.1)
-output num
The top N sentence hypothesis to be output at the end of
search. Use with -n (default: 1)
-lookuprange frame
Set the number of frames before and after to look up next
word hypotheses in the word trellis on the second pass. This prevents the
omission of short words, but with a large value, the number of expanded
hypotheses increases and system becomes slow. (default: 5)
-looktrellis
(Grammar) Expand only the words survived on the first
pass instead of expanding all the words predicted by grammar. This option
makes second pass decoding faster especially for large vocabulary condition,
but may increase deletion error of short words. (default: disabled)
Short-pause segmentation / decoder-VAD
When compiled with --enable-decoder-vad, the short-pause
segmentation will be extended to support decoder-based VAD.
-spsegment
Enable short-pause segmentation mode. Input will be
segmented when a short pause word (word with only silence model in
pronunciation) gets the highest likelihood at certain successive frames on the
first pass. When detected segment end, Julius stop the 1st pass at the point,
perform 2nd pass, and continue with next segment. The word context will be
considered among segments. (Rev.4.0)
When compiled with --enable-decoder-vad, this option enables
decoder-based VAD, to skip long silence.
-spdur frame
Short pause duration length to detect end of input
segment, in number of frames. (default: 10)
-pausemodels string
A comma-separated list of pause model names to be used at
short-pause segmentation. The word whose pronunciation consists of only the
pause models will be treated as "pause word" and used for pause
detection. If not specified, name of -spmodel, -silhead and
-siltail will be used. (Rev.4.0)
-spmargin frame
Back step margin at trigger up for decoder-based VAD.
When speech up-trigger found by decoder-VAD, Julius will rewind the input
parameter by this value, and start recognition at the point. (Rev.4.0)
This option will be valid only if compiled with
--enable-decoder-vad.
-spdelay frame
Trigger decision delay frame at trigger up for
decoder-based VAD. (Rev.4.0)
This option will be valid only if compiled with
--enable-decoder-vad.
Word lattice / confusion network output
-lattice , -nolattice
Enable / disable generation of word graph. Search
algorithm also has changed to optimize for better word graph generation, so
the sentence result may not be the same as normal N-best recognition.
(Rev.4.0)
-confnet , -noconfnet
Enable / disable generation of confusion network.
Enabling this will also activates -lattice internally. (Rev.4.0)
-graphrange frame
Merge same words at neighbor position at graph
generation. If the beginning time and ending time of two word candidates of
the same word is within the specified range, they will be merged. The default
is 0 (allow merging same words on exactly the same location) and specifying
larger value will result in smaller graph output. Setting this value to -1
will disable merging, in that case same words on the same location of
different scores will be left as they are. (default: 0)
-graphcut depth
Cut the resulting graph by its word depth at
post-processing stage. The depth value is the number of words to be allowed at
a frame. Setting to -1 disables this feature. (default: 80)
-graphboundloop count
Limit the number of boundary adjustment loop at
post-processing stage. This parameter prevents Julius from blocking by
infinite adjustment loop by short word oscillation. (default: 20)
-graphsearchdelay , -nographsearchdelay
When this option is enabled, Julius modifies its graph
generation algorithm on the 2nd pass not to terminate search by graph merging,
until the first sentence candidate is found. This option may improve graph
accuracy, especially when you are going to generate a huge word graph by
setting broad search. Namely, it may result in better graph accuracy when you
set wide beams on both 1st pass -b and 2nd pass -b2, and large
number for -n. (default: disabled)
Multi-gram / multi-dic recognition
-multigramout , -nomultigramout
On grammar recognition using multiple grammars, Julius
will output only the best result among all grammars. Enabling this option will
make Julius to output result for each grammar. (default: disabled)
Forced alignment
-walign
Do viterbi alignment per word units for the recognition
result. The word boundary frames and the average acoustic scores per frame
will be calculated.
-palign
Do viterbi alignment per phone units for the recognition
result. The phone boundary frames and the average acoustic scores per frame
will be calculated.
-salign
Do viterbi alignment per state for the recognition
result. The state boundary frames and the average acoustic scores per frame
will be calculated.
Misc. search options
-inactive
Start this recognition process instance with inactive
state. (Rev.4.0)
-1pass
Perform only the first pass.
-fallback1pass
When 2nd pass fails, Julius finish the recognition with
no result. This option tell Julius to output the 1st pass result as a final
result when the 2nd pass fails. Note that some score output (confidence etc.)
may not be useful. This was the default behavior of Julius-3.x.
-no_ccd , -force_ccd
Explicitly switch phone context handling at search.
Normally Julius determines whether the using AM is a context-dependent model
or not from the model names, i.e., whether the names contain character + and
-. This option will override the automatic detection.
-cmalpha float
Smoothing parameter for confidence scoring. (default:
0.05)
-iwsp
(Multi-path mode only) Enable inter-word context-free
short pause insertion. This option appends a skippable short pause model for
every word end. The short-pause model can be specified by
-spmodel.
-transp float
Additional insertion penalty for transparent words.
(default: 0.0)
-demo
Equivalent to -progout -quiet.
ALSADEV
(using mic input with alsa device) specify a capture
device name. If not specified, "default" will be used.
AUDIODEV
(using mic input with oss device) specify a capture
device path. If not specified, "/dev/dsp" will be used.
LATENCY_MSEC
Try to set input latency of microphone input in
milliseconds. Smaller value will shorten latency but sometimes make process
unstable. Default value will depend on the running OS.
For examples of system usage, refer to the tutorial section in the Julius
documents.
Note about jconf files: relative paths in a jconf file are interpreted as
relative to the jconf file itself, not to the current directory.
julian(1), jcontrol(1), adinrec(1), adintool(1),
mkbingram(1), mkbinhmm(1), mkgsmm(1), wav2mfcc(1),
mkss(1)
http://julius.sourceforge.jp/en/
Julius normally will return the exit status 0. If an error occurs, Julius exits
abnormally with exit status 1. If an input file cannot be found or cannot be
loaded for some reason then Julius will skip processing for that file.
There are some restrictions to the type and size of the models Julius can use.
For a detailed explanation refer to the Julius documentation. For bug-reports,
inquires and comments please contact julius-info at lists.sourceforge.jp.
Copyright (c) 1991-2008 Kawahara Lab., Kyoto University
Copyright (c) 1997-2000 Information-technology Promotion Agency,
Japan
Copyright (c) 2000-2008 Shikano Lab., Nara Institute of Science
and Technology
Copyright (c) 2005-2008 Julius project team, Nagoya Institute of
Technology
Rev.1.0 (1998/02/20)
Designed by Tatsuya KAWAHARA and Akinobu LEE (Kyoto
University)
Development by Akinobu LEE (Kyoto University)
Rev.1.1 (1998/04/14), Rev.1.2 (1998/10/31), Rev.2.0 (1999/02/20),
Rev.2.1 (1999/04/20), Rev.2.2 (1999/10/04), Rev.3.0 (2000/02/14), Rev.3.1
(2000/05/11)
Development of above versions by Akinobu LEE (Kyoto
University)
Rev.3.2 (2001/08/15), Rev.3.3 (2002/09/11), Rev.3.4 (2003/10/01),
Rev.3.4.1 (2004/02/25), Rev.3.4.2 (2004/04/30)
Development of above versions by Akinobu LEE (Nara
Institute of Science and Technology)
Rev.3.5 (2005/11/11), Rev.3.5.1 (2006/03/31), Rev.3.5.2
(2006/07/31), Rev.3.5.3 (2006/12/29), Rev.4.0 (2007/12/19), Rev.4.1
(2008/10/03)
Development of above versions by Akinobu LEE (Nagoya
Institute of Technology)
From rev.3.2, Julius is released by the "Information Processing Society,
Continuous Speech Consortium".
The Windows DLL version was developed and released by Hideki BANNO
(Nagoya University).
The Windows Microsoft Speech API compatible version was developed
by Takashi SUMIYOSHI (Kyoto University).
 Visit the GSP FreeBSD Man Page Interface.
Output converted with ManDoc.
|
