NAME
julius
- open source multi-purpose LVCSR engine
SYNOPSIS
julius [-C jconffile] [options...]
DESCRIPTION
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.
Supported Models
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(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.
Search Algorithm
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.
OPTIONS
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.
Julius application option
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.
Global options
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.
Instance declaration for multi decoding
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)
Language model (-LM)
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.
Acoustic model and feature analysis (-AM) (-AM_GMM)
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.
Recognition process and search (-SR)
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.
ENVIRONMENT VARIABLES
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.
EXAMPLES
For examples of system usage, refer to the tutorial section in the
Julius documents.
NOTICE
Note about jconf files: relative paths in a jconf file are interpreted
as relative to the jconf file itself, not to the current directory.
SEE ALSO
julian(1), jcontrol(1), adinrec(1), adintool(1), mkbingram(1),
mkbinhmm(1), mkgsmm(1), wav2mfcc(1), mkss(1)
http://julius.sourceforge.jp/en/
DIAGNOSTICS
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.
BUGS
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
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
AUTHORS
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)
THANKS TO
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).
02/11/2009