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POCKETSPHINX(1) General Commands Manual POCKETSPHINX(1)

NAME

pocketsphinx - Run speech recognition on audio data

SYNOPSIS

pocketsphinx [ options... ] [ live | single | help | soxflags ] INPUTS...

DESCRIPTION

The ‘pocketsphinx’ command-line program reads single-channel 16-bit PCM audio one or more input files (or ‘-’ to read from standard input), and attempts to recognize speech in it using the default acoustic and language model. The input files can be raw audio, WAV, or NIST Sphere files, though some of these may not be recognized properly. It accepts a large number of options which you probably don't care about, and a command which defaults to ‘live’. The commands are as follows:

Print a long list of those options you don't care about.
Dump configuration as JSON to standard output (can be loaded with the ‘-config’ option).
Detect speech segments in input files, run recognition on them (using those options you don't care about), and write the results to standard output in line-delimited JSON. I realize this isn't the prettiest format, but it sure beats XML. Each line contains a JSON object with these fields, which have short names to make the lines more readable:
"b": Start time in seconds, from the beginning of the stream
"d": Duration in seconds
"p": Estimated probability of the recognition result, i.e. a number between 0 and 1 which may be used as a confidence score
"t": Full text of recognition result
"w": List of segments (usually words), each of which in turn contains the ‘b’, ‘d’, ‘p’, and ‘t’ fields, for start, end, probability, and the text of the word. In the future we may also support hierarchical results in which case ‘w’ could be present.
Recognize the input as a single utterance, and write a JSON object in the same format described above.

Align a single input file (or ‘-’ for standard input) to a word sequence, and write a JSON object in the same format described above. The first positional argument is the input, and all subsequent ones are concatenated to make the text, to avoid surprises if you forget to quote it. You are responsible for normalizing the text to remove punctuation, uppercase, centipedes, etc. For example:



    pocketsphinx align goforward.wav "go forward ten meters"

By default, only word-level alignment is done. To get phone alignments, pass `-phone_align yes` in the flags, e.g.:



    pocketsphinx -phone_align yes align audio.wav $text

This will make not particularly readable output, but you can use jq (https://stedolan.github.io/jq/) to clean it up. For example, you can get just the word names and start times like this:



    pocketsphinx align audio.wav $text | jq '.w[]|[.t,.b]'

Or you could get the phone names and durations like this:



    pocketsphinx -phone_align yes align audio.wav $text | jq '.w[]|.w[]|[.t,.d]'

There are many, many other possibilities, of course.

Print a usage and help text with a list of possible arguments.
Return arguments to ‘sox’ which will create the appropriate input format. Note that because the ‘sox’ command-line is slightly quirky these must always come after the filename or ‘-d’ (which tells ‘sox’ to read from the microphone). You can run live recognition like this: 



    sox -d $(pocketsphinx soxflags) | pocketsphinx -

or decode from a file named "audio.mp3" like this:



    sox audio.mp3 $(pocketsphinx soxflags) | pocketsphinx -

By default only errors are printed to standard error, but if you want more information you can pass ‘-loglevel INFO’. Partial results are not printed, maybe they will be in the future, but don't hold your breath. Force-alignment is likely to be supported soon, however.

OPTIONS

Automatic gain control for c0 ('max', 'emax', 'noise', or 'none')
Initial threshold for automatic gain control
phoneme decoding with phonetic lm (given here)
Perform phoneme decoding with phonetic lm and context-independent units only
Preemphasis parameter
Inverse of acoustic model scale for confidence score calculation
Inverse weight applied to acoustic scores.
Print results and backtraces to log.
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 ('live', 'batch', or 'none')
Initial values (comma-separated) for cepstral mean when 'live' is used
Compute all senone scores in every frame (can be faster when there are many senones)
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.
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 keyphrase 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
Minimum level of log messages (DEBUG, INFO, WARN, ERROR)
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, or 0 to set automatically (recommended)
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 using spectral subtraction
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)
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

AUTHOR

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

COPYRIGHT

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

SEE ALSO

pocketsphinx_batch(1), sphinx_fe(1).

2022-09-27