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pocketsphinx_batch - Man Page

Run speech recognition in batch mode


pocketsphinx_batch -ctl ctlfile -cepdir cepdir -cepext .mfc [ options ]...


Run speech recognition over a list of utterances in batchmode.  A list of arguments follows:


Size of audio file header in bytes (headers are ignored)


Input is raw audio data


Automatic gain control for c0 ('max', 'emax', 'noise', or 'none')


Initial threshold for automatic gain control


phoneme decoding with phonetic lm


Perform phoneme decoding with phonetic lm and context-independent units only


Preemphasis parameter


file giving extra arguments.


Inverse of acoustic model scale for confidence score calculation


Inverse weight applied to acoustic scores.


Print results and backtraces to log file.


Beam width applied to every frame in Viterbi search (smaller values mean wider beam)


Run bestpath (Dijkstra) search over word lattice (3rd pass)


Language model probability weight for bestpath search


Create missing subdirectories in output directory


files directory (prefixed to filespecs in control file)


Input files extension (suffixed to filespecs in control file)


Number of components in the input feature vector


Cepstral mean normalization scheme ('current', 'prior', or 'none')


Initial values (comma-separated) for cepstral mean when 'prior' is used


Compute all senone scores in every frame (can be faster when there are many senones)


file listing utterances to be processed


No. of utterances to be processed (after skipping -ctloffset entries)


Do every Nth line in the control file


No. of utterances at the beginning of -ctl file to be skipped


output in CTM file format (may require post-sorting)


level for debugging messages


pronunciation dictionary (lexicon) input file


Dictionary is case sensitive (NOTE: case insensitivity applies to ASCII characters only)


Add 1/2-bit noise


Use double bandwidth filters (same center freq)


Frame GMM computation downsampling ratio


word pronunciation dictionary input file


Feature stream type, depends on the acoustic model


containing feature extraction parameters.


Filler word transition probability


Frame rate


format finite state grammar file


file listing FSG file to use for each utterance


directory for FSG files


extension for FSG files (including leading dot)


Add alternate pronunciations to FSG


Insert filler words at each state.


Run forward flat-lexicon search over word lattice (2nd pass)


Beam width applied to every frame in second-pass flat search


Minimum number of end frames for a word to be searched in fwdflat search


Language model probability weight for flat lexicon (2nd pass) decoding


Window of frames in lattice to search for successor words in fwdflat search


Beam width applied to word exits in second-pass flat search


Run forward lexicon-tree search (1st pass)


containing acoustic model files.


output file name


output with segmentation file name


Endianness of input data, big or little, ignored if NIST or MS Wav


grammar file


to spot


file with keyphrases to spot, one per line


Delay to wait for best detection score


Phone loop probability for keyword spotting


Threshold for p(hyp)/p(alternatives) ratio


Initial backpointer table size


containing transformation matrix to be applied to features (single-stream features only)


Dimensionality of output of feature transformation (0 to use entire matrix)


Length of sin-curve for liftering, or 0 for no liftering.


trigram language model input file


a set of language model


language model in -lmctl to use by default


file listing LM name to use for each utterance


Base in which all log-likelihoods calculated


to write log messages in


Write out logspectral files instead of cepstra


Lower edge of filters


Beam width applied to last phone in words


Beam width applied to last phone in single-phone words


Language model probability weight


Maximum number of active HMMs to maintain at each frame (or -1 for no pruning)


Maximum number of distinct word exits at each frame (or -1 for no pruning)


definition input file


gaussian means input file


to log feature files to


Nodes ignored in lattice construction if they persist for fewer than N frames


mixture weights input file (uncompressed)


Senone mixture weights floor (applied to data from -mixw file)


transformation to apply to means and variances


file listing MLLR transforms to use for each utterance


directory for MLLR transforms


extension for MLLR transforms (including leading dot)


Use memory-mapped I/O (if possible) for model files


Number of N-best hypotheses to write to -nbestdir (0 for no N-best)


for writing N-best hypothesis lists


Extension for N-best hypothesis list files


Number of cep coefficients


Size of FFT


Number of filter banks


New word transition penalty


Minimum posterior probability for output lattice nodes


for dumping word lattices


Filename extension for dumping word lattices


Format for dumping word lattices (s3 or htk)


Beam width applied to phone transitions


Phone insertion penalty


Beam width applied to phone loop search for lookahead


Beam width applied to phone loop transitions for lookahead


Phone insertion penalty for phone loop


Weight for phoneme lookahead penalties


Phoneme lookahead window size, in frames


to log raw audio files to


Remove DC offset from each frame


Remove noise with spectral subtraction in mel-energies


Round mel filter frequencies to DFT points


Sampling rate


Seed for random number generator; if less than zero, pick our own


dump (compressed mixture weights) input file


Input is senone score dump files


to log senone score files to


to codebook mapping input file (usually not needed)


Silence word transition probability


Write out cepstral-smoothed logspectral files


specification (e.g., 24,0-11/25,12-23/26-38 or 0-12/13-25/26-38)


state transition matrix input file


HMM state transition probability floor (applied to -tmat file)


Maximum number of top Gaussians to use in scoring.


Beam width used to determine top-N Gaussians (or a list, per-feature)


rule for JSGF (first public rule is default)


Which type of transform to use to calculate cepstra (legacy, dct, or htk)


Normalize mel filters to unit area


Upper edge of filters


Unigram weight


gaussian variances input file


Mixture gaussian variance floor (applied to data from -var file)


Variance normalize each utterance (only if CMN == current)


Show input filenames


defining the warping function


Warping function type (or shape)


Beam width applied to word exits


Word insertion penalty


Hamming window length

To do batchmode recognition, you will need to specify a control file, using -ctl This is a simple text file containing one entry per line.  Each entry is the name of an input file relative to the -cepdir directory, and without the filename extension (which is given in the -cepext argument).

If you are using acoustic feature files as input (see sphinx_fe(1) for information on how to generate these), you can also specify a subpart of a file, using the following format:



Written by numerous people at CMU from 1994 onwards.  This manual page by David Huggins-Daines <dhdaines@gmail.com>

See Also

pocketsphinx_continuous(1), sphinx_fe(1).

Referenced By