Provided by: julius_4.2.2-0ubuntu2_amd64 
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.
-chunk_size samples
Audio fragment size in number of samples. (default: 1000)
-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 JULIUS(1)