- IF options are conflicting or appears several times, the last one will overrides former ones.
- The option "-C
jconffile" will expand the content of the jconf file at the position. - In jconf file, relative path arguments are treated as relative to the jconf file itself, not the current working directory.
Options are categorized into one of the following groups:
APPLMAMSRGLOBAL
APP group is application-side option group. The options in the APP group can be specified for the command-line application "julius", but not available at your application integrating libjulius and libsent. They can be specified at any point of the option sequence.
LM, AM and SR are option groups for language model, acoustic model and search algorithm, respectively.
GLOBAL group contains misc. options that are independent from models and algorithms. They contains options concerning audio front-end and utility back-end.
You do not need to care about the groups when you just use Julius with single recognition instance (1 AM, 1 LM). If you are going to set up Julius to perform multi-instance decoding with multiple models, you should care about the groups and section declaration as described in the following section.
Julius supports multi-instance decoding with multiple models. The multi-instance decoding runs each models simultaneously for an audio input within a single thread and output multiple results at once.
To perform multi-model decoding, you should define several model sets, and also define search instances using the options. To specify multiple models sets and instances, a special option -AM, -LM and -SR can be used to switch the mode. Below is an example of jconf file for multi-model decoding:
# Example of two ASR module, each has its original AM/LM
(GLOBAL options)
...
-AM Mname1
(AM options for Mname1)
-AM Mname2
(AM options for Mname2)
...
-LM Lname1
(LM options for Lname1)
...
-LM Lname2
(LM options for Lname2)
...
-SR module1 Mname1 Lname1
(SR options for module1)
...
-SR module2 Mname2 Lname2
(SR options for module1)
...
-GLOBAL
(GLOBAL options)
# Example of two ASR modules with different LMs sharing single AM
(GLOBAL options)
...
-AM name
(AM options for name)
-LM Lname1
(LM options for Lname1)
...
-LM Lname2
(LM options for Lname2)
...
-SR module1 name Lname1
(SR options for module1)
...
-SR module2 name Lname2
(SR options for module1)
...
-GLOBAL
(GLOBAL options)
When using the switches, the GLOBAL options should be placed either at the top (before any instance declaration), or after -GLOBAL option.
Create a new AM configuration set, and switch current to the new one. You should give a unique name. (Rev.4.0)
Create a new LM configuration set, and switch current to the new one. You should give a unique name. (Rev.4.0)
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)
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)
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)
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 declaration. This is enabled by default. (Rev.4.1)
On file input, save 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)
Output the language and acoustic scores separately.
Print the callback names at each call for debug. (rev.4.0)
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 code page number. Code name should be one of "ansi", "mac", "oem", "utf-7", "utf-8", "sjis", "euc". Or you can specify any code page number supported at your environment.
Disable character conversion.
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.
For module mode, choose which information to be output to module client. arg is a sequence of characters to be output:
W: wordL: LM entryP: phone sequenceS: scoreC: confidence scorew: 1st pass wordl: 1st pass LM entryp: 1st pass phone sequences: 1st pass score
Default is "WLPS", which means to output word, LM entry, phone sequence and score of the final result.
For module mode, disable XML special character escaping (Rev.4.5).
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 monaural WAV. Invalid for mfcc file input.
With input rejection by -rejectshort, the rejected input will also
be recorded even if they are rejected.
Save all log output to a file instead of standard output. (Rev.4.0)
Disable all log output. (Rev.4.0)
Output help message and exit.
Choose speech input source. Specify 'file' or 'rawfile' for
waveform file, 'htkparam' or 'mfcfile' for HTK parameter file,
'outprob' for outprob vectors from 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 tcp-ip
network from an adinnet client. 'netaudio' is from
DatLink/NetAudio input, and 'stdin' means data input from
standard input. 'vecnet' means receiving feature / outprob
vectors via tcp-ip network from vecnet client.
For waveform file input, only WAV (no compression) and RAW
(no header, 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)
On some OS, instead of mic, you can explicitly specify available audio API (alsa/oss/esd/...).
(With -input rawfile|mfcfile|outprob) 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. See also -outfile for per-input result output.
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)
Host name for DatLink server input (-input netaudio).
With -input adinnet, specify adinnet port number to listen.
(default: 5530)
Julius by default removes successive zero samples in input speech data. This option disables the zero sample removal.
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.
Posterior scaling of magnitude of audio input.
Turn on / off the speech detection by level and zero-cross. Default is on for mic / adinnet input, and off for files.
Level threshold for speech input detection. Values should be in range from 0 to 32767. (default: 2000)
Zero crossing threshold per second. Only input that goes over
the level threshold (-lv) will be counted. (default: 60)
Silence margin at the start of speech segment in milliseconds. (default: 300)
Silence margin at the end of speech segment in milliseconds. (default: 400)
Buffer length of the audio input can be set with number of samples (default number is 1000). If you set small number, you can reduce the delay. However, it becomes unstable when too small.
Set libfvad-based VAD mode. mode is an integer value from -1 to 3, specify -1 to disable, 0 for moderate detection, 3 for most aggressive detection (more likely to drop speech-like noises). Default value is -1 (disabled)
Set libfvad detailed parameter. nFrame is the number of smoothing frame. threshold is the threshold to detect speech trigger [0.0-1.0]. Default values are 5 and 0.5 respectively.
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.
Reject input shorter than specified milliseconds. Search will be terminated and no result will be output.
Reject input longer than specified milliseconds. Search will be terminated and no result will be output.
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.
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 -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 detect down-trigger when it gets
lower than a value of -gmmdown.
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.
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 may cause degradation of identification performance.
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.
(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.
(GMM_VAD) Up trigger threshold of voice activity score. (Rev.4.1)
This option will be valid only if compiled with
--enable-gmm-vad.
(GMM_VAD) Down trigger threshold of voice activity score. (Rev.4.1)
This option will be valid only if compiled with
--enable-gmm-vad.
Explicitly switch between "stream processing" and "buffered processing". "-realtime" sets mode to stream processing, and "-norealtime" sets mode to buffered processing.
Default is buffer processing for files, and stream processing for microphone and network input. Setting "-realtime" to a file input can simulate the recognition process as if it were input from microphone.
Load a jconf file at here. The content of the jconffile will be expanded at this point.
Print version information to standard error, and exit.
Print engine setting information to standard error, and exit.
Output less log. For result, only the best word sequence will be printed.
(For debug) output enormous internal message and debug information to log.
For debug, enter interactive check mode.
Specify directory to load plugin. If several directories exist, specify them by colon-separated list.
Save computed outprob vectors to HTK file (for debug).
Use binary format N-gram. An ARPA N-gram file can be converted to Julius binary format by mkbingram.
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)
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)
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 also use -nogram to reset the grammars already specified before
the point.
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.
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.
An old way of specifying grammar files separately. This is bogus, and should not be used any more.
Remove the current list of grammars already specified by -gram,
-gramlist, -dfa and -v.
Word dictionary file.
Silence word defined in the dictionary, for silences at the
beginning of sentence and end of sentence. (default: "{<s>",
"</s>")
Specify unknown word. Default is "<unk>" and "<UNK>". This will
be used to assign word probability on unknown words, i.e. words
in dictionary that are not in N-gram vocabulary.
Skip error words in dictionary and force running.
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.
Specify the word entry that will be added by -iwspword.
(default: "<UNK> [sp] sp sp")
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)
Load grammars in additional on startup.
Load entry of words in additional on startup.
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.
Word dictionary for isolated word recognition. File format is the same as other LM. (Rev.4.0)
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)
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)
Declare to use user LM functions in the program. This option should be specified if you use user-defined LM functions. (Rev.4.0)
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.
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.
Specify the number of top Gaussians to be calculated in a mixture code book. Small number will speed up the acoustic computation, but AM accuracy may get worse with too small value. See also -gprune. (default: 2)
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")
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)
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
code book 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).
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.
Insertion penalty for word-end short pauses appended by -iwsp.
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.
On GMS, specify number of monophone states to compute corresponding triphones in detail. (default: 24)
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.
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.
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.
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.
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.
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.
Pre-emphasis coefficient. (default: 0.97)
This option corresponds to the HTK Option PREEMCOEF. The same
value can be given to this option.
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 default (22).
Cepstral liftering coefficient. (default: 22)
This option corresponds to the HTK Option CEPLIFTER. The same
value can be given to this option.
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).
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).
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).
Energy silence floor in dB when normalizing log energy. (default: 50.0)
This option corresponds to the HTK Option SILFLOOR.
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.
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.
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.
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.
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)
Use power instead of magnitude on filterbank analysis. (default: disabled)
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 cepstral mean of the input is unknown at the start. So MAP-CMN will be used. On MAP-CMN, a generic 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.
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. When specified, the save/load of the CMN file by "-cmnload" and "-cmnsave" also contains variance information to give its initial generic values.
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.
Load generic cepstral mean vector (and variance when invoked with -cvn) from file on startup. The
file should be one saved by -cmnsave. Loading an generic
cepstral mean enables Julius to better recognize the first
utterance on a real-time input. It is also required to specify this option when using -cmnstatic or -cvnstatic. When used together with
-cmnnoupdate, the value in the file will be used for all input.
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.
Control whether to update the generic cepstral mean on
real-time input. The update is enabled by default, the stored cepstral
mean will be updated using the last 5 seconds of last input, and the
updated mean will be used as the initial value on the next input for MAP-CMN. Disabling the update
by -cmnnoupdate causes Julius to always start its MAP-CMN
at every input from the initial generic value, either 0 or the one given
by -cmnload. Enabled by default.
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)
Perform static mean and variance normalization. When specified, Julius always use the loaded mean and variance for all input and all frames. This option works for both buffered input and stream input. Requires the static mean and variance to be
loaded by -cmnload.
Perform static variance normalization only. Like -cmnstatic but only variance is static and mean will be computed as normal. Requires the static variance to be loaded by -cmnload. If static mean exists in the loaded file, it will be ignored.
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.
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.
With -sscalc, specify the length of head silence for noise
spectrum estimation in milliseconds. (default: 300)
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.
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)
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)
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.
(N-gram) Language model weights and word insertion penalties for the first pass.
(Grammar) word insertion penalty for the first pass. (default: 0.0)
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)
Score width for score pruning on first pass. This option can be
used together with rank beaming (-b width). The default state
is not active.
Upper limit of token per node. This option is valid when
--enable-wpair and --enable-wpair-nlimit are enabled at
compilation time.
Enable progressive output of the partial results on the first pass.
Set the time interval for -progout in milliseconds. (default:
300)
(N-gram) Language model weights and word insertion penalties for the second pass.
(Grammar) word insertion penalty for the second pass. (default: 0.0)
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)
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)
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)
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)
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)
The top N sentence hypothesis to be output at the end of
search. Use with -n (default: 1)
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)
(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)
When compiled with --enable-decoder-vad, the short-pause
segmentation will be extended to support decoder-based VAD.
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.
Short pause duration length to detect end of input segment, in number of frames. (default: 10)
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)
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.
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.
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)
Enable / disable generation of confusion network. Enabling this
will also activates -lattice internally. (Rev.4.0)
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)
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)
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)
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
wider beams on both 1st pass -b and 2nd pass -b2, and larger
value for -n. (default: disabled)
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)
Do viterbi alignment per word units for the recognition result. The word boundary frames and the average acoustic scores per frame will be calculated.
Do viterbi alignment per phone units for the recognition result. The phone boundary frames and the average acoustic scores per frame will be calculated.
Do viterbi alignment per state for the recognition result. The state boundary frames and the average acoustic scores per frame will be calculated.
Start this recognition process instance with inactive state. (Rev.4.0)
Perform only the first pass.
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.
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.
Smoothing parameter for confidence scoring. (default: 0.05)
(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.
Additional insertion penalty for transparent words. (default: 0.0)
Equivalent to -progout -quiet.