Hi,
Do the values of the mfcc file extracted with sphinx_fe represent the feature vector ? What I mean here is, are there any other calculations before constructing the mfcc matrix ?
Last edit: Karim BEN ALAYA 2017-08-04
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What do those values (mfcc file read with matlab) refer to exactely ? Are they only ceptrums ?
I noticed that in each file, the numbers are a multiplication of 39, So I guess it's the whole set in there, right ? (ceptrum + delta + delta-delta) And are they in the right order of the feat 1s_c_d_dd ?
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Yes it does, the sphinx_fe script takes the wav input file ( speech signal) and convert it to acoustic features or feature vector, take a look to man sphinx_fe which produce as a result :
NAME
sphinx_fe - Convert audio files to acoustic feature files
SYNOPSIS
sphinx_fe [ options ]...
DESCRIPTION
This program converts audio files (in either Microsoft WAV, NIST Sphere, or raw format) to acoustic feature files for input
to batch-mode speech recognition. The resulting files are also useful for various other things. A list of options follows:
-alpha Preemphasis parameter
-argfile
file (e.g. feat.params from an acoustic model) to read parameters from. This will override anything set in other
command line arguments.
-blocksize
Number of samples to read at a time.
-build_outdirs
Create missing subdirectories in output directory
-c file for batch processing
-cep2spec
Input is cepstral files, output is log spectral files
-di directory, input file names are relative to this, if defined
-dither
Add 1/2-bit noise
-do directory, output files are relative to this
-doublebw
Use double bandwidth filters (same center freq)
-ei extension to be applied to all input files
-eo extension to be applied to all output files
-example
Shows example of how to use the tool
-frate Frame rate
-help Shows the usage of the tool
-i audio input file
-input_endian
Endianness of input data, big or little, ignored if NIST or MS Wav
-lifter
Length of sin-curve for liftering, or 0 for no liftering.
-logspec
Write out logspectral files instead of cepstra
-lowerf
Lower edge of filters
-mach_endian
Endianness of machine, big or little
-mswav Defines input format as Microsoft Wav (RIFF)
-ncep Number of cep coefficients
-nchans
Number of channels of data (interlaced samples assumed)
-nfft Size of FFT
-nfilt Number of filter banks
-nist Defines input format as NIST sphere
-npart Number of parts to run in (supersedes -nskip and -runlen if non-zero)
-nskip If a control file was specified, the number of utterances to skip at the head of the file
-o cepstral output file
-ofmt Format of output files - one of sphinx, htk, text.
-part Index of the part to run (supersedes -nskip and -runlen if non-zero)
-raw Defines input format as raw binary data
-remove_dc
Remove DC offset from each frame
-remove_noise
Remove noise with spectral subtraction in mel-energies
-remove_silence
Enables VAD, removes silence frames from processing
-round_filters
Round mel filter frequencies to DFT points
-runlen
If a control file was specified, the number of utterances to process, or -1 for all
-samprate
Sampling rate
-seed Seed for random number generator; if less than zero, pick our own
-smoothspec
Write out cepstral-smoothed logspectral files
-spec2cep
Input is log spectral files, output is cepstral files
-sph2pipe
Input is NIST sphere (possibly with Shorten), use sph2pipe to convert
-transform
Which type of transform to use to calculate cepstra (legacy, dct, or htk)
-unit_area
Normalize mel filters to unit area
-upperf
Upper edge of filters
-vad_postspeech
Num of silence frames to keep after from speech to silence.
-vad_prespeech
Num of speech frames to keep before silence to speech.
-vad_startspeech
Num of speech frames to trigger vad from silence to speech.
-vad_threshold
Threshold for decision between noise and silence frames. Log-ratio between signal level and noise level.
-verbose
Show input filenames
-warp_params
defining the warping function
-warp_type
Warping function type (or shape)
-whichchan
Channel to process (numbered from 1), or 0 to mix all channels
-wlen Hamming window length
Currently the only kind of features supported are MFCCs (mel-frequency cepstral coefficients). There are numerous options
which control the properties of the output features. It is VERY important that you document the specific set of flags used
to create any given set of feature files, since this information is NOT recorded in the files themselves, and any mismatch
between the parameters used to extract features for recognition and those used to extract features for training will cause
recognition to fail.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
Hi,
Do the values of the mfcc file extracted with sphinx_fe represent the feature vector ? What I mean here is, are there any other calculations before constructing the mfcc matrix ?
Last edit: Karim BEN ALAYA 2017-08-04
What do those values (mfcc file read with matlab) refer to exactely ? Are they only ceptrums ?
I noticed that in each file, the numbers are a multiplication of 39, So I guess it's the whole set in there, right ? (ceptrum + delta + delta-delta) And are they in the right order of the feat 1s_c_d_dd ?
Yes it does, the sphinx_fe script takes the wav input file ( speech signal) and convert it to acoustic features or feature vector, take a look to man sphinx_fe which produce as a result :
NAME
sphinx_fe - Convert audio files to acoustic feature files
SYNOPSIS
sphinx_fe [ options ]...
DESCRIPTION
This program converts audio files (in either Microsoft WAV, NIST Sphere, or raw format) to acoustic feature files for input
to batch-mode speech recognition. The resulting files are also useful for various other things. A list of options follows:
-alpha Preemphasis parameter -argfile file (e.g. feat.params from an acoustic model) to read parameters from. This will override anything set in other command line arguments. -blocksize Number of samples to read at a time. -build_outdirs Create missing subdirectories in output directory -c file for batch processing -cep2spec Input is cepstral files, output is log spectral files -di directory, input file names are relative to this, if defined -dither Add 1/2-bit noise -do directory, output files are relative to this -doublebw Use double bandwidth filters (same center freq) -ei extension to be applied to all input files -eo extension to be applied to all output files -example Shows example of how to use the tool -frate Frame rate -help Shows the usage of the tool -i audio input file -input_endian Endianness of input data, big or little, ignored if NIST or MS Wav -lifter Length of sin-curve for liftering, or 0 for no liftering. -logspec Write out logspectral files instead of cepstra -lowerf Lower edge of filters -mach_endian Endianness of machine, big or little -mswav Defines input format as Microsoft Wav (RIFF) -ncep Number of cep coefficients -nchans Number of channels of data (interlaced samples assumed) -nfft Size of FFT -nfilt Number of filter banks -nist Defines input format as NIST sphere -npart Number of parts to run in (supersedes -nskip and -runlen if non-zero) -nskip If a control file was specified, the number of utterances to skip at the head of the file -o cepstral output file -ofmt Format of output files - one of sphinx, htk, text. -part Index of the part to run (supersedes -nskip and -runlen if non-zero) -raw Defines input format as raw binary data -remove_dc Remove DC offset from each frame -remove_noise Remove noise with spectral subtraction in mel-energies -remove_silence Enables VAD, removes silence frames from processing -round_filters Round mel filter frequencies to DFT points -runlen If a control file was specified, the number of utterances to process, or -1 for all -samprate Sampling rate -seed Seed for random number generator; if less than zero, pick our own -smoothspec Write out cepstral-smoothed logspectral files -spec2cep Input is log spectral files, output is cepstral files -sph2pipe Input is NIST sphere (possibly with Shorten), use sph2pipe to convert -transform Which type of transform to use to calculate cepstra (legacy, dct, or htk) -unit_area Normalize mel filters to unit area -upperf Upper edge of filters -vad_postspeech Num of silence frames to keep after from speech to silence. -vad_prespeech Num of speech frames to keep before silence to speech. -vad_startspeech Num of speech frames to trigger vad from silence to speech. -vad_threshold Threshold for decision between noise and silence frames. Log-ratio between signal level and noise level. -verbose Show input filenames -warp_params defining the warping function -warp_type Warping function type (or shape) -whichchan Channel to process (numbered from 1), or 0 to mix all channels -wlen Hamming window length Currently the only kind of features supported are MFCCs (mel-frequency cepstral coefficients). There are numerous options which control the properties of the output features. It is VERY important that you document the specific set of flags used to create any given set of feature files, since this information is NOT recorded in the files themselves, and any mismatch between the parameters used to extract features for recognition and those used to extract features for training will cause recognition to fail.