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pocketsphinx_continuous - Run speech recognition in continuous listening mode


pocketsphinx_continuous [-infile filename.wav ] [ -inmic yes ] [ options ]...


This program opens the audio device or a file and waits for speech. When it detects an utterance, it performs speech recognition on it.

To record from microphone and decode use

-inmic yes

To decode a 16kHz 16-bit mono WAV file use

-infile filename.wav

You can also specify -lm or -fsg or -kws depending on whether you are using a statistical language model or a finite-state grammar or look for a keyphase.


of audio device to use for input.
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
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)
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
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.
file to transcribe.
Transcribe audio from microphone.
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
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
Use memory-mapped I/O (if possible) for model files
Number of cep coefficients
Size of FFT
Number of filter banks
New word transition penalty
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
Enables VAD, removes silence frames from processing
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
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)
Print word times in file transcription.
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
Num of silence frames to keep after from speech to silence.
Num of speech frames to keep before silence to speech.
Num of speech frames to trigger vad from silence to speech.
Threshold for decision between noise and silence frames. Log-ratio between signal level and noise level.
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


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


Copyright © 1994-2016 Carnegie Mellon University. See the file LICENSE included with this package for more information.


pocketsphinx_batch(1), sphinx_fe(1).